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Trostle JA, Robbins C, Corozo Angulo B, Acevedo A, Coloma J, Eisenberg JNS. "Dengue fever is not just urban or rural: Reframing its spatial categorization.". Soc Sci Med 2024; 362:117384. [PMID: 39393331 DOI: 10.1016/j.socscimed.2024.117384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2024] [Revised: 09/26/2024] [Accepted: 09/30/2024] [Indexed: 10/13/2024]
Abstract
Infectious diseases exploit niches that are often spatially defined as urban and/or rural. Yet spatial research on infectious diseases often fails to define "urban" and "rural" and how these contexts might influence their epidemiology. We use dengue fever, thought to be mostly an urban disease with rural foci, as a device to explore local definitions of urban and rural spaces and the impact of these spaces on dengue risk in the provinfine urban and rural locales. Interviews conducted from 2019 to 2021 with 71 residents and 23 health personce of Esmeraldas, Ecuador. Ecuador, like many countries, only uses population size and administrative function to denel found that they identified the availability of basic services, extent of their control over their environment, and presence of underbrush and weeds (known in Ecuador as monte and maleza and conceptualized in this paper as natural disorder) as important links to their conceptions of space and dengue risk. This broader conceptualization of space articulated by local residents and professionals reflects a more sophisticated approach to characterizing dengue risk than using categories of urban and rural employed by the national census and government. Rather than this dichotomous category of space, dengue fever can be better framed for health interventions in terms of specific environmental features and assemblages of high-risk spaces. An understanding of how community members perceive risk enhances our ability to collaborate with them to develop optimal mitigation strategies.
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Affiliation(s)
- James A Trostle
- Anthropology Department, Trinity College, 300 Summit St, Hartford, CT, 06106, United states.
| | - Charlotte Robbins
- Departments of Environmental Science and Urban Studies, Trinity College, United states.
| | | | | | | | - Joseph N S Eisenberg
- School of Public Health, University of Michigan and Universidad San Francisco de Quito, Ecuador.
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2
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Brook CE, Rozins C, Bohl JA, Ahyong V, Chea S, Fahsbender L, Huy R, Lay S, Leang R, Li Y, Lon C, Man S, Oum M, Northrup GR, Oliveira F, Pacheco AR, Parker DM, Young K, Boots M, Tato CM, DeRisi JL, Yek C, Manning JE. Climate, demography, immunology, and virology combine to drive two decades of dengue virus dynamics in Cambodia. Proc Natl Acad Sci U S A 2024; 121:e2318704121. [PMID: 39190356 PMCID: PMC11388344 DOI: 10.1073/pnas.2318704121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 07/31/2024] [Indexed: 08/28/2024] Open
Abstract
The incidence of dengue virus disease has increased globally across the past half-century, with highest number of cases ever reported in 2019 and again in 2023. We analyzed climatological, epidemiological, and phylogenomic data to investigate drivers of two decades of dengue in Cambodia, an understudied endemic setting. Using epidemiological models fit to a 19-y dataset, we first demonstrate that climate-driven transmission alone is insufficient to explain three epidemics across the time series. We then use wavelet decomposition to highlight enhanced annual and multiannual synchronicity in dengue cycles between provinces in epidemic years, suggesting a role for climate in homogenizing dynamics across space and time. Assuming reported cases correspond to symptomatic secondary infections, we next use an age-structured catalytic model to estimate a declining force of infection for dengue through time, which elevates the mean age of reported cases in Cambodia. Reported cases in >70-y-old individuals in the 2019 epidemic are best explained when also allowing for waning multitypic immunity and repeat symptomatic infections in older patients. We support this work with phylogenetic analysis of 192 dengue virus (DENV) genomes that we sequenced between 2019 and 2022, which document emergence of DENV-2 Cosmopolitan Genotype-II into Cambodia. This lineage demonstrates phylogenetic homogeneity across wide geographic areas, consistent with invasion behavior and in contrast to high phylogenetic diversity exhibited by endemic DENV-1. Finally, we simulate an age-structured, mechanistic model of dengue dynamics to demonstrate how expansion of an antigenically distinct lineage that evades preexisting multitypic immunity effectively reproduces the older-age infections witnessed in our data.
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Affiliation(s)
- Cara E Brook
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637
| | - Carly Rozins
- Department of Science, Technology, and Society, York University, Toronto, ON M3J 1P3, Canada
| | - Jennifer A Bohl
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, NIH, Rockville, MD 20892
| | - Vida Ahyong
- Chan Zuckerberg Biohub, San Francisco, CA 94158
| | - Sophana Chea
- International Center of Excellence in Research, National Institute of Allergy and Infectious Diseases, NIH, Phnom Penh 120801, Cambodia
| | | | - Rekol Huy
- National Center for Parasitology, Entomology, and Malaria Control, Phnom Penh 120801, Cambodia
| | - Sreyngim Lay
- International Center of Excellence in Research, National Institute of Allergy and Infectious Diseases, NIH, Phnom Penh 120801, Cambodia
| | - Rithea Leang
- National Center for Parasitology, Entomology, and Malaria Control, Phnom Penh 120801, Cambodia
| | - Yimei Li
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637
| | - Chanthap Lon
- International Center of Excellence in Research, National Institute of Allergy and Infectious Diseases, NIH, Phnom Penh 120801, Cambodia
| | - Somnang Man
- International Center of Excellence in Research, National Institute of Allergy and Infectious Diseases, NIH, Phnom Penh 120801, Cambodia
- National Center for Parasitology, Entomology, and Malaria Control, Phnom Penh 120801, Cambodia
| | - Mengheng Oum
- International Center of Excellence in Research, National Institute of Allergy and Infectious Diseases, NIH, Phnom Penh 120801, Cambodia
| | - Graham R Northrup
- Center for Computational Biology, University of California, Berkeley, CA 94720
| | - Fabiano Oliveira
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, NIH, Rockville, MD 20892
| | - Andrea R Pacheco
- International Center of Excellence in Research, National Institute of Allergy and Infectious Diseases, NIH, Phnom Penh 120801, Cambodia
| | - Daniel M Parker
- Department of Population Health and Disease Prevention, University of California, Irvine, CA 92697
- Department of Epidemiology and Biostatistics, University of California, Irvine, CA 92697
| | - Katherine Young
- Department of Biological Sciences, University of Texas, El Paso, TX 79968
| | - Michael Boots
- Department of Integrative Biology, University of California, Berkeley, CA 94720
| | | | | | - Christina Yek
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, NIH, Rockville, MD 20892
- International Center of Excellence in Research, National Institute of Allergy and Infectious Diseases, NIH, Phnom Penh 120801, Cambodia
| | - Jessica E Manning
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, NIH, Rockville, MD 20892
- International Center of Excellence in Research, National Institute of Allergy and Infectious Diseases, NIH, Phnom Penh 120801, Cambodia
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Miao Y, Zhang W, Li Y, Wu J, Shen Z, Bai J, Zhu D, Ren R, Zhang J, Guo D, Tarimo CS, Li C, Dong W. Quantifying the benefits of healthy lifestyle behaviors and emotional expressivity in lowering the risk of COVID-19 infection: a national survey of Chinese population. BMC Public Health 2023; 23:2374. [PMID: 38037040 PMCID: PMC10687789 DOI: 10.1186/s12889-023-17158-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 11/04/2023] [Indexed: 12/02/2023] Open
Abstract
BACKGROUND COVID-19 is still prevalent in most countries around the world at the low level. Residents' lifestyle behaviors and emotions are critical to prevent COVID-19 and keep healthy, but there is lacking of confirmative evidence on how residents' lifestyle behaviors and emotional expressivity affected COVID-19 infection. METHODS Baseline study was conducted in August 2022 and follow-up study was conducted in February 2023. Baseline survey collected information on residents' basic information, as well as their lifestyle behaviors and emotions. Follow-up study was carried out to gather data on COVID-19 infection condition. Binary logistic regression was utilized to identify factors that may influence COVID-19 infection. Attributable risk (AR) was computed to determine the proportion of unhealthy lifestyle behaviors and emotional factors that could be attributed to COVID-19 infection. Sensitivity analysis was performed to test the robustness of the results. RESULTS A total of 5776 participants (46.57% males) were included in this study, yielding an overall COVID-19 infection rate of 54.8% (95%CI: 53.5 - 56.0%). The findings revealed that higher stress levels [aOR = 1.027 (95%CI; 1.005-1.050)] and lower frequency in wearing masks, washing hands, and keeping distance [aOR = 1.615 (95%CI; 1.087-2.401)], were positively associated with an increased likelihood of COVID-19 infection (all P < 0.05). If these associations were causal, 8.1% of COVID-19 infection would have been prevented if all participants had normal stress levels [Attributable Risk Percentage: 8.1% (95%CI: 5.9-10.3%)]. A significant interaction effect between stress and the frequency in wearing masks, washing hands, and keeping distance on COVID-19 infection was observed (β = 0.006, P < 0.001), which also was independent factor of COVID-19 infection. CONCLUSIONS The overall COVID-19 infection rate among residents is at a medium level. Residents' increasing stress and decreasing frequency in wearing masks and washing hands and keeping distance contribute to increasing risk of infection, residents should increase the frequency of mask-wearing, practice hand hygiene, keep safe distance from others, ensure stable emotional state, minimize psychological stress, providing evidence support for future responses to emerging infectious diseases.
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Affiliation(s)
- Yudong Miao
- Department of Health Management, College of Public Health, Zhengzhou University, Henan, China
| | - Wanliang Zhang
- Department of Health Management, College of Public Health, Zhengzhou University, Henan, China
| | - Yi Li
- Department of Health Management, College of Public Health, Zhengzhou University, Henan, China
| | - Jian Wu
- Department of Health Management, College of Public Health, Zhengzhou University, Henan, China
| | - Zhanlei Shen
- Department of Health Management, College of Public Health, Zhengzhou University, Henan, China
| | - Junwen Bai
- Department of Health Management, College of Public Health, Zhengzhou University, Henan, China
| | - Dongfang Zhu
- Department of Health Management, College of Public Health, Zhengzhou University, Henan, China
| | - Ruizhe Ren
- Department of Health Management, College of Public Health, Zhengzhou University, Henan, China
| | - Jingbao Zhang
- Department of Health Management, College of Public Health, Zhengzhou University, Henan, China
| | - Dan Guo
- Department of Neurology, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Henan, China
| | - Clifford Silver Tarimo
- Department of Science and Laboratory Technology, Dar es salaam Institute of Technology, Dar es Salaam, Tanzania
| | - Chengpeng Li
- Department of Human Resources, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Henan, China
| | - Wenyong Dong
- Department of Hypertension, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Henan, China.
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Castelli C, Castellini M, Comincioli N, Parisi ML, Pontarollo N, Vergalli S. Ecosystem degradation and the spread of Covid-19. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:836. [PMID: 37308607 PMCID: PMC10260383 DOI: 10.1007/s10661-023-11403-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 05/17/2023] [Indexed: 06/14/2023]
Abstract
The linkages between the emergence of zoonotic diseases and ecosystem degradation have been widely acknowledged by the scientific community and policy makers. In this paper we investigate the relationship between human overexploitation of natural resources, represented by the Human Appropriation of Net Primary Production Index (HANPP) and the spread of Covid-19 cases during the first pandemic wave in 730 regions of 63 countries worldwide. Using a Bayesian estimation technique, we highlight the significant role of HANPP as a driver of Covid-19 diffusion, besides confirming the well-known impact of population size and the effects of other socio-economic variables. We believe that these findings could be relevant for policy makers in their effort towards a more sustainable intensive agriculture and responsible urbanisation.
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Affiliation(s)
- Chiara Castelli
- The Vienna Institute for International Economic Studies, Vienna, Austria
| | - Marta Castellini
- Department of Economics and Management "Marco Fanno", University of Padua, Padua, Italy
- Fondazione Eni Enrico Mattei, Milan, Italy
| | - Nicola Comincioli
- Fondazione Eni Enrico Mattei, Milan, Italy
- Department of Economics and Management, University of Brescia, Brescia, Italy
| | - Maria Laura Parisi
- Department of Economics and Management, University of Brescia, Brescia, Italy
| | - Nicola Pontarollo
- Department of Economics and Management, University of Brescia, Brescia, Italy.
| | - Sergio Vergalli
- Fondazione Eni Enrico Mattei, Milan, Italy
- Department of Economics and Management, University of Brescia, Brescia, Italy
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Man O, Kraay A, Thomas R, Trostle J, Lee GO, Robbins C, Morrison AC, Coloma J, Eisenberg JNS. Characterizing dengue transmission in rural areas: A systematic review. PLoS Negl Trop Dis 2023; 17:e0011333. [PMID: 37289678 PMCID: PMC10249895 DOI: 10.1371/journal.pntd.0011333] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023] Open
Abstract
Dengue has historically been considered an urban disease associated with dense human populations and the built environment. Recently, studies suggest increasing dengue virus (DENV) transmission in rural populations. It is unclear whether these reports reflect recent spread into rural areas or ongoing transmission that was previously unnoticed, and what mechanisms are driving this rural transmission. We conducted a systematic review to synthesize research on dengue in rural areas and apply this knowledge to summarize aspects of rurality used in current epidemiological studies of DENV transmission given changing and mixed environments. We described how authors defined rurality and how they defined mechanisms for rural dengue transmission. We systematically searched PubMed, Web of Science, and Embase for articles evaluating dengue prevalence or cumulative incidence in rural areas. A total of 106 articles published between 1958 and 2021 met our inclusion criteria. Overall, 56% (n = 22) of the 48 estimates that compared urban and rural settings reported rural dengue incidence as being as high or higher than in urban locations. In some rural areas, the force of infection appears to be increasing over time, as measured by increasing seroprevalence in children and thus likely decreasing age of first infection, suggesting that rural dengue transmission may be a relatively recent phenomenon. Authors characterized rural locations by many different factors, including population density and size, environmental and land use characteristics, and by comparing their context to urban areas. Hypothesized mechanisms for rural dengue transmission included travel, population size, urban infrastructure, vector and environmental factors, among other mechanisms. Strengthening our understanding of the relationship between rurality and dengue will require a more nuanced definition of rurality from the perspective of DENV transmission. Future studies should focus on characterizing details of study locations based on their environmental features, exposure histories, and movement dynamics to identify characteristics that may influence dengue transmission.
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Affiliation(s)
- Olivia Man
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Alicia Kraay
- Department of Kinesiology and Community Health, University of Illinois, Urbana, Illinois, United States of America
- Institution for Genomic Biology, University of Illinois, Urbana, Illinois, United States of America
| | - Ruth Thomas
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - James Trostle
- Department of Anthropology, Trinity College, Hartford, Connecticut, United States of America
| | - Gwenyth O. Lee
- Rutgers Global Health Institute, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, United States of America
- Rutgers Department of Biostatistics and Epidemiology, School of Public Health, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, United States of America
| | - Charlotte Robbins
- Department of Anthropology, Trinity College, Hartford, Connecticut, United States of America
| | - Amy C. Morrison
- Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, Davis, California, United States of America
| | - Josefina Coloma
- Division of Infectious Diseases and Vaccinology, School of Public Health, University of California, Berkeley, Berkeley, California, United States of America
| | - Joseph N. S. Eisenberg
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
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6
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Lozano S, Pritts K, Duguma D, Fredregill C, Connelly R. Independent evaluation of Wolbachia infected male mosquito releases for control of Aedes aegypti in Harris County, Texas, using a Bayesian abundance estimator. PLoS Negl Trop Dis 2022; 16:e0010907. [PMID: 36374939 PMCID: PMC9704758 DOI: 10.1371/journal.pntd.0010907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 11/28/2022] [Accepted: 10/23/2022] [Indexed: 11/15/2022] Open
Abstract
Among disease vectors, Aedes aegypti (L.) (Diptera: Culicidae) is one of the most insidious species in the world. The disease burden created by this species has dramatically increased in the past 50 years, and during this time countries have relied on pesticides for control and prevention of viruses borne by Ae. aegypti. The small number of available insecticides with different modes of action had led to increases in insecticide resistance, thus, strategies, like the "Incompatible Insect Technique" using Wolbachia's cytoplasmic incompatibility are desirable. We evaluated the effect of releases of Wolbachia infected Ae. aegypti males on populations of wild Ae. aegypti in the metropolitan area of Houston, TX. Releases were conducted by the company MosquitoMate, Inc. To estimate mosquito population reduction, we used a mosquito abundance Bayesian hierarchical estimator that accounted for inefficient trapping. MosquitoMate previously reported a reduction of 78% for an intervention conducted in Miami, FL. In this experiment we found a reduction of 93% with 95% credibility intervals of 86% and 96% after six weeks of continual releases. A similar result was reported by Verily Life Sciences, 96% [94%, 97%], in releases made in Fresno, CA.
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Affiliation(s)
- Saul Lozano
- National Center for Emerging and Zoonotic Infectious Diseases, Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado, United States of America
| | - Kevin Pritts
- Western Gulf Center of Excellence for Vector-Borne Diseases, University of Texas Medical Branch, Galveston, Texas, United States of America
| | - Dagne Duguma
- Harris County Public Health, Mosquito and Vector Control Division, Houston, Texas, United States of America
| | - Chris Fredregill
- Harris County Public Health, Mosquito and Vector Control Division, Houston, Texas, United States of America
| | - Roxanne Connelly
- National Center for Emerging and Zoonotic Infectious Diseases, Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado, United States of America
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7
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Gutierrez JA, Laneri K, Aparicio JP, Sibona GJ. Meteorological indicators of dengue epidemics in non-endemic Northwest Argentina. Infect Dis Model 2022; 7:823-834. [DOI: 10.1016/j.idm.2022.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 10/11/2022] [Accepted: 10/12/2022] [Indexed: 11/07/2022] Open
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Sekarrini CE, Sumarmi S, Bachri S, Taryana D, Giofandi EA. Euclidean Distance Modeling of Musi River in Controlling the Dengue Epidemic Transmission in Palembang City. Open Access Maced J Med Sci 2022. [DOI: 10.3889/oamjms.2022.9125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND: Various attempts have been made to control the population of Aedes aegypti with the help of chemicals or by engineering Wolbachia pipentis, an obligate intracellular bacterium that is passed down through DENV and arbovirus infections to manipulate the monthly average reproductive yield. This study reviews the phenomenon of the river border area which is one of the habitats for the Aedes aegypti mosquito in the Musi River, Palembang City.
AIM: The application of the euclidean distance method in this study was carried out to determine the environmental exposure of settlements along the river basin area.
METHODS: The research methodology was carried out objectively related to data on dengue incidence in 2019. It was carried out by taking location coordinates through the application of geographic information systems and the use of satellite imagery for data acquisition of existing buildings. This stage is followed by bivariate statistical calculations using the application of WoE where the probability value of the measurement is described using the Area Under Curve. Processing and accumulation carried out with existing buildings will result in a calculation of the estimated size of the exposure area.
RESULTS: The results obtained provide information, where the natural breaks jeanks value of 0.007-0.016 range results in 1465ha of heavily exposed building area. The value of the temporary bivariate statistical calculation will produce an AUC probability number of 0.44 which describes the relationship between the Musi river and the findings of dengue symptoms in the sub-districts around the Musi river border area, Palembang City. Swamp soil conditions are vulnerable to being a habitat where Aedes aegypti larvae are found.
CONCLUSIONS: Based on the analysis that we obtained from the population of dengue incidence and the condition of the river basin area showed a significant structure with the distribution of dengue incidence, it is known that the presence of buildings on the river Musi banks has a greater risk of infectious diseases transmissions and natural disasters ranging from sanitation, hygiene, flooding to river erosion.
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Huang Q, Liu Q, Song C, Liu X, Shu H, Wang X, Liu Y, Chen X, Chen J, Pei T. Urban spatial epidemic simulation model: A case study of the second COVID-19 outbreak in Beijing, China. TRANSACTIONS IN GIS : TG 2022; 26:297-316. [PMID: 34899033 PMCID: PMC8646780 DOI: 10.1111/tgis.12850] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
The second COVID-19 outbreak in Beijing was controlled by non-pharmaceutical interventions, which avoided a second pandemic. Until mass vaccination achieves herd immunity, cities are at risk of similar outbreaks. It is vital to quantify and simulate Beijing's non-pharmaceutical interventions to find effective intervention policies for the second outbreak. Few models have achieved accurate intra-city spatio-temporal epidemic spread simulation, and most modeling studies focused on the initial pandemic. We built a dynamic module of infected case movement within the city, and established an urban spatially epidemic simulation model (USESM), using mobile phone signaling data to create scenarios to assess the impact of interventions. We found that: (1) USESM simulated the transmission process of the epidemic within Beijing; (2) USESM showed the epidemic curve and presented the spatial distribution of epidemic spread on a map; and (3) to balance resources, interventions, and economic development, nucleic acid testing intensity could be increased and restrictions on human mobility in non-epidemic areas eased.
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Affiliation(s)
- Qiang Huang
- State Key Laboratory of Resources and Environmental Information SystemInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Qiyong Liu
- State Key Laboratory of Infectious Disease Prevention and ControlCollaborative Innovation Center for Diagnosis and Treatment of Infectious DiseasesWHO Collaborating Centre for Vector Surveillance and ManagementNational Institute for Communicable Disease Control and PreventionChinese Center for Disease Control and PreventionBeijingChina
| | - Ci Song
- State Key Laboratory of Resources and Environmental Information SystemInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Xiaobo Liu
- State Key Laboratory of Infectious Disease Prevention and ControlCollaborative Innovation Center for Diagnosis and Treatment of Infectious DiseasesWHO Collaborating Centre for Vector Surveillance and ManagementNational Institute for Communicable Disease Control and PreventionChinese Center for Disease Control and PreventionBeijingChina
| | - Hua Shu
- State Key Laboratory of Resources and Environmental Information SystemInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Xi Wang
- State Key Laboratory of Resources and Environmental Information SystemInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Yaxi Liu
- State Key Laboratory of Resources and Environmental Information SystemInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Xiao Chen
- State Key Laboratory of Resources and Environmental Information SystemInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Jie Chen
- State Key Laboratory of Resources and Environmental Information SystemInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Tao Pei
- State Key Laboratory of Resources and Environmental Information SystemInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and ApplicationNanjingChina
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10
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Lima FT, Brown NC, Duarte JP. Understanding the Impact of Walkability, Population Density, and Population Size on COVID-19 Spread: A Pilot Study of the Early Contagion in the United States. ENTROPY 2021; 23:e23111512. [PMID: 34828210 PMCID: PMC8619267 DOI: 10.3390/e23111512] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 11/10/2021] [Accepted: 11/10/2021] [Indexed: 12/18/2022]
Abstract
The novel coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global event that has been challenging governments, health systems, and communities worldwide. Available data from the first months indicated varying patterns of the spread of COVID-19 within American cities, when the spread was faster in high-density and walkable cities such as New York than in low-density and car-oriented cities such as Los Angeles. Subsequent containment efforts, underlying population characteristics, variants, and other factors likely affected the spread significantly. However, this work investigates the hypothesis that urban configuration and associated spatial use patterns directly impact how the disease spreads and infects a population. It follows work that has shown how the spatial configuration of urban spaces impacts the social behavior of people moving through those spaces. It addresses the first 60 days of contagion (before containment measures were widely adopted and had time to affect spread) in 93 urban counties in the United States, considering population size, population density, walkability, here evaluated through walkscore, an indicator that measures the density of amenities, and, therefore, opportunities for population mixing, and the number of confirmed cases and deaths. Our findings indicate correlations between walkability, population density, and COVID-19 spreading patterns but no clear correlation between population size and the number of cases or deaths per 100 k habitants. Although virus spread beyond these initial cases may provide additional data for analysis, this study is an initial step in understanding the relationship between COVID-19 and urban configuration.
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Affiliation(s)
- Fernando T. Lima
- Stuckeman Center for Design Computing, The Pennsylvania State University, University Park, State College, PA 16802, USA;
- Faculty of Architecture and Urbanism, Universidade Federal de Juiz de Fora, Juiz de Fora, MG 36036-900, Brazil
- Correspondence:
| | - Nathan C. Brown
- Department of Architectural Engineering, The Pennsylvania State University, University Park, State College, PA 16802, USA;
| | - José P. Duarte
- Stuckeman Center for Design Computing, The Pennsylvania State University, University Park, State College, PA 16802, USA;
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11
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Carlin EP, Allen KC, Morgan JJ, Chretien JP, Murray S, Winslow D, Zimmerman D. Behavioral Risk Modeling for Pandemics: Overcoming Challenges and Advancing the Science. Health Secur 2021; 19:447-453. [PMID: 34415788 DOI: 10.1089/hs.2020.0209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Ellen P Carlin
- Ellen P. Carlin, DVM, was a Senior Health and Policy Specialist, EcoHealth Alliance, New York, NY. She is now an Assistant Research Professor, Georgetown University Center for Global Health Science and Security, Washington, DC. Koya C. Allen, PhD, MS, MSPH, was an Infectious Disease Subject Matter Expert, European Command Headquarters, US Department of Defense, Stuttgart, Germany. She is now a Scientific Officer, Barcelona Institute for Global Health, Malaria Eradication Scientific Alliance, Barcelona, Spain. Jeffrey J. Morgan, MS, was a Senior Systems Engineer, Joint Research and Development, Stafford, VA. He is currently a Senior Systems Engineer, iPower, LLC, Reston, VA, and a PhD Student, Biomedical Engineering, Catholic University of America, Washington, DC. Jean-Paul Chretien, MD, PhD, is a Program Manager, Defense Advanced Research Program Agency, Arlington, VA. Suzan Murray, DVM, DACZM, is Program Director and Dawn Zimmerman, DVM, MS, is Director of Wildlife Health and Associate Program Director; both in the Global Health Program, Smithsonian Conservation Biology Institute, Washington, DC. Dawn Zimmerman is also an Adjunct Assistant Professor, Department of Epidemiology and Microbial Disease, Yale School of Public Health, New Haven, CT. Deborah Winslow, PhD, is a Senior Scholar, School for Advanced Research, Santa Fe, NM
| | - Koya C Allen
- Ellen P. Carlin, DVM, was a Senior Health and Policy Specialist, EcoHealth Alliance, New York, NY. She is now an Assistant Research Professor, Georgetown University Center for Global Health Science and Security, Washington, DC. Koya C. Allen, PhD, MS, MSPH, was an Infectious Disease Subject Matter Expert, European Command Headquarters, US Department of Defense, Stuttgart, Germany. She is now a Scientific Officer, Barcelona Institute for Global Health, Malaria Eradication Scientific Alliance, Barcelona, Spain. Jeffrey J. Morgan, MS, was a Senior Systems Engineer, Joint Research and Development, Stafford, VA. He is currently a Senior Systems Engineer, iPower, LLC, Reston, VA, and a PhD Student, Biomedical Engineering, Catholic University of America, Washington, DC. Jean-Paul Chretien, MD, PhD, is a Program Manager, Defense Advanced Research Program Agency, Arlington, VA. Suzan Murray, DVM, DACZM, is Program Director and Dawn Zimmerman, DVM, MS, is Director of Wildlife Health and Associate Program Director; both in the Global Health Program, Smithsonian Conservation Biology Institute, Washington, DC. Dawn Zimmerman is also an Adjunct Assistant Professor, Department of Epidemiology and Microbial Disease, Yale School of Public Health, New Haven, CT. Deborah Winslow, PhD, is a Senior Scholar, School for Advanced Research, Santa Fe, NM
| | - Jeffrey J Morgan
- Ellen P. Carlin, DVM, was a Senior Health and Policy Specialist, EcoHealth Alliance, New York, NY. She is now an Assistant Research Professor, Georgetown University Center for Global Health Science and Security, Washington, DC. Koya C. Allen, PhD, MS, MSPH, was an Infectious Disease Subject Matter Expert, European Command Headquarters, US Department of Defense, Stuttgart, Germany. She is now a Scientific Officer, Barcelona Institute for Global Health, Malaria Eradication Scientific Alliance, Barcelona, Spain. Jeffrey J. Morgan, MS, was a Senior Systems Engineer, Joint Research and Development, Stafford, VA. He is currently a Senior Systems Engineer, iPower, LLC, Reston, VA, and a PhD Student, Biomedical Engineering, Catholic University of America, Washington, DC. Jean-Paul Chretien, MD, PhD, is a Program Manager, Defense Advanced Research Program Agency, Arlington, VA. Suzan Murray, DVM, DACZM, is Program Director and Dawn Zimmerman, DVM, MS, is Director of Wildlife Health and Associate Program Director; both in the Global Health Program, Smithsonian Conservation Biology Institute, Washington, DC. Dawn Zimmerman is also an Adjunct Assistant Professor, Department of Epidemiology and Microbial Disease, Yale School of Public Health, New Haven, CT. Deborah Winslow, PhD, is a Senior Scholar, School for Advanced Research, Santa Fe, NM
| | - Jean-Paul Chretien
- Ellen P. Carlin, DVM, was a Senior Health and Policy Specialist, EcoHealth Alliance, New York, NY. She is now an Assistant Research Professor, Georgetown University Center for Global Health Science and Security, Washington, DC. Koya C. Allen, PhD, MS, MSPH, was an Infectious Disease Subject Matter Expert, European Command Headquarters, US Department of Defense, Stuttgart, Germany. She is now a Scientific Officer, Barcelona Institute for Global Health, Malaria Eradication Scientific Alliance, Barcelona, Spain. Jeffrey J. Morgan, MS, was a Senior Systems Engineer, Joint Research and Development, Stafford, VA. He is currently a Senior Systems Engineer, iPower, LLC, Reston, VA, and a PhD Student, Biomedical Engineering, Catholic University of America, Washington, DC. Jean-Paul Chretien, MD, PhD, is a Program Manager, Defense Advanced Research Program Agency, Arlington, VA. Suzan Murray, DVM, DACZM, is Program Director and Dawn Zimmerman, DVM, MS, is Director of Wildlife Health and Associate Program Director; both in the Global Health Program, Smithsonian Conservation Biology Institute, Washington, DC. Dawn Zimmerman is also an Adjunct Assistant Professor, Department of Epidemiology and Microbial Disease, Yale School of Public Health, New Haven, CT. Deborah Winslow, PhD, is a Senior Scholar, School for Advanced Research, Santa Fe, NM
| | - Suzan Murray
- Ellen P. Carlin, DVM, was a Senior Health and Policy Specialist, EcoHealth Alliance, New York, NY. She is now an Assistant Research Professor, Georgetown University Center for Global Health Science and Security, Washington, DC. Koya C. Allen, PhD, MS, MSPH, was an Infectious Disease Subject Matter Expert, European Command Headquarters, US Department of Defense, Stuttgart, Germany. She is now a Scientific Officer, Barcelona Institute for Global Health, Malaria Eradication Scientific Alliance, Barcelona, Spain. Jeffrey J. Morgan, MS, was a Senior Systems Engineer, Joint Research and Development, Stafford, VA. He is currently a Senior Systems Engineer, iPower, LLC, Reston, VA, and a PhD Student, Biomedical Engineering, Catholic University of America, Washington, DC. Jean-Paul Chretien, MD, PhD, is a Program Manager, Defense Advanced Research Program Agency, Arlington, VA. Suzan Murray, DVM, DACZM, is Program Director and Dawn Zimmerman, DVM, MS, is Director of Wildlife Health and Associate Program Director; both in the Global Health Program, Smithsonian Conservation Biology Institute, Washington, DC. Dawn Zimmerman is also an Adjunct Assistant Professor, Department of Epidemiology and Microbial Disease, Yale School of Public Health, New Haven, CT. Deborah Winslow, PhD, is a Senior Scholar, School for Advanced Research, Santa Fe, NM
| | - Deborah Winslow
- Ellen P. Carlin, DVM, was a Senior Health and Policy Specialist, EcoHealth Alliance, New York, NY. She is now an Assistant Research Professor, Georgetown University Center for Global Health Science and Security, Washington, DC. Koya C. Allen, PhD, MS, MSPH, was an Infectious Disease Subject Matter Expert, European Command Headquarters, US Department of Defense, Stuttgart, Germany. She is now a Scientific Officer, Barcelona Institute for Global Health, Malaria Eradication Scientific Alliance, Barcelona, Spain. Jeffrey J. Morgan, MS, was a Senior Systems Engineer, Joint Research and Development, Stafford, VA. He is currently a Senior Systems Engineer, iPower, LLC, Reston, VA, and a PhD Student, Biomedical Engineering, Catholic University of America, Washington, DC. Jean-Paul Chretien, MD, PhD, is a Program Manager, Defense Advanced Research Program Agency, Arlington, VA. Suzan Murray, DVM, DACZM, is Program Director and Dawn Zimmerman, DVM, MS, is Director of Wildlife Health and Associate Program Director; both in the Global Health Program, Smithsonian Conservation Biology Institute, Washington, DC. Dawn Zimmerman is also an Adjunct Assistant Professor, Department of Epidemiology and Microbial Disease, Yale School of Public Health, New Haven, CT. Deborah Winslow, PhD, is a Senior Scholar, School for Advanced Research, Santa Fe, NM
| | - Dawn Zimmerman
- Ellen P. Carlin, DVM, was a Senior Health and Policy Specialist, EcoHealth Alliance, New York, NY. She is now an Assistant Research Professor, Georgetown University Center for Global Health Science and Security, Washington, DC. Koya C. Allen, PhD, MS, MSPH, was an Infectious Disease Subject Matter Expert, European Command Headquarters, US Department of Defense, Stuttgart, Germany. She is now a Scientific Officer, Barcelona Institute for Global Health, Malaria Eradication Scientific Alliance, Barcelona, Spain. Jeffrey J. Morgan, MS, was a Senior Systems Engineer, Joint Research and Development, Stafford, VA. He is currently a Senior Systems Engineer, iPower, LLC, Reston, VA, and a PhD Student, Biomedical Engineering, Catholic University of America, Washington, DC. Jean-Paul Chretien, MD, PhD, is a Program Manager, Defense Advanced Research Program Agency, Arlington, VA. Suzan Murray, DVM, DACZM, is Program Director and Dawn Zimmerman, DVM, MS, is Director of Wildlife Health and Associate Program Director; both in the Global Health Program, Smithsonian Conservation Biology Institute, Washington, DC. Dawn Zimmerman is also an Adjunct Assistant Professor, Department of Epidemiology and Microbial Disease, Yale School of Public Health, New Haven, CT. Deborah Winslow, PhD, is a Senior Scholar, School for Advanced Research, Santa Fe, NM
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Schaber KL, Morrison AC, Elson WH, Astete-Vega H, Córdova-López JJ, Ríos López EJ, Flores WLQ, Santillan ASV, Scott TW, Waller LA, Kitron U, Barker CM, Perkins TA, Rothman AL, Vazquez-Prokopec GM, Elder JP, Paz-Soldan VA. The impact of dengue illness on social distancing and caregiving behavior. PLoS Negl Trop Dis 2021; 15:e0009614. [PMID: 34280204 PMCID: PMC8354465 DOI: 10.1371/journal.pntd.0009614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 08/10/2021] [Accepted: 06/30/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Human mobility among residential locations can drive dengue virus (DENV) transmission dynamics. Recently, it was shown that individuals with symptomatic DENV infection exhibit significant changes in their mobility patterns, spending more time at home during illness. This change in mobility is predicted to increase the risk of acquiring infection for those living with or visiting the ill individual. It has yet to be considered, however, whether social contacts are also changing their mobility, either by socially distancing themselves from the infectious individual or increasing contact to help care for them. Social, or physical, distancing and caregiving could have diverse yet important impacts on DENV transmission dynamics; therefore, it is necessary to better understand the nature and frequency of these behaviors including their effect on mobility. METHODOLOGY AND PRINCIPAL FINDINGS Through community-based febrile illness surveillance and RT-PCR infection confirmation, 67 DENV positive (DENV+) residents were identified in the city of Iquitos, Peru. Using retrospective interviews, data were collected on visitors and home-based care received during the illness. While 15% of participants lost visitors during their illness, 22% gained visitors; overall, 32% of all individuals (particularly females) received visitors while symptomatic. Caregiving was common (90%), particularly caring by housemates (91%) and caring for children (98%). Twenty-eight percent of caregivers changed their behavior enough to have their work (and, likely, mobility patterns) affected. This was significantly more likely when caring for individuals with low "health-related quality of well-being" during illness (Fisher's Exact, p = 0.01). CONCLUSIONS/SIGNIFICANCE Our study demonstrates that social contacts of individuals with dengue modify their patterns of visitation and caregiving. The observed mobility changes could impact a susceptible individual's exposure to virus or a presymptomatic/clinically inapparent individual's contribution to onward transmission. Accounting for changes in social contact mobility is imperative in order to get a more accurate understanding of DENV transmission.
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Affiliation(s)
- Kathryn L. Schaber
- Program of Population Biology, Ecology and Evolution, Emory University, Atlanta, Georgia, United States of America
| | - Amy C. Morrison
- Department of Virology and Emerging Infections, U.S. Naval Medical Research Unit No. 6, Lima and Iquitos, Peru
- Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, California, United States of America
| | - William H. Elson
- Department of Entomology and Nematology, University of California, Davis, California, United States of America
| | - Helvio Astete-Vega
- Department of Virology and Emerging Infections, U.S. Naval Medical Research Unit No. 6, Lima and Iquitos, Peru
| | - Jhonny J. Córdova-López
- Department of Entomology and Nematology, University of California, Davis, California, United States of America
| | - Esther Jennifer Ríos López
- Department of Virology and Emerging Infections, U.S. Naval Medical Research Unit No. 6, Lima and Iquitos, Peru
| | - W. Lorena Quiroz Flores
- Department of Virology and Emerging Infections, U.S. Naval Medical Research Unit No. 6, Lima and Iquitos, Peru
| | | | - Thomas W. Scott
- Department of Entomology and Nematology, University of California, Davis, California, United States of America
| | - Lance A. Waller
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Uriel Kitron
- Department of Environmental Sciences, Emory University, Atlanta, Georgia, United States of America
| | - Christopher M. Barker
- Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, California, United States of America
| | - T. Alex Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Alan L. Rothman
- Institute for Immunology and Informatics and Department of Cell and Molecular Biology, University of Rhode Island, Providence, Rhode Island, United States of America
| | - Gonzalo M. Vazquez-Prokopec
- Program of Population Biology, Ecology and Evolution, Emory University, Atlanta, Georgia, United States of America
- Department of Environmental Sciences, Emory University, Atlanta, Georgia, United States of America
| | - John P. Elder
- School of Public Health, San Diego State University, San Diego, California, United States of America
| | - Valerie A. Paz-Soldan
- Department of Global Community Health and Behavioral Sciences, Tulane School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
- * E-mail:
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13
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Lee SA, Jarvis CI, Edmunds WJ, Economou T, Lowe R. Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions. J R Soc Interface 2021; 18:20210096. [PMID: 34034534 PMCID: PMC8150046 DOI: 10.1098/rsif.2021.0096] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 04/26/2021] [Indexed: 12/14/2022] Open
Abstract
Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.
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Affiliation(s)
- Sophie A. Lee
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Christopher I. Jarvis
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - W. John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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14
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Johansen IC, Castro MCD, Alves LC, Carmo RLD. Population mobility, demographic, and environmental characteristics of dengue fever epidemics in a major city in Southeastern Brazil, 2007-2015. CAD SAUDE PUBLICA 2021; 37:e00079620. [PMID: 33886707 DOI: 10.1590/0102-311x00079620] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Accepted: 07/31/2020] [Indexed: 11/22/2022] Open
Abstract
Around 14% of world dengue virus (DENV) cases occur in the Americas, most of them in Brazil. While socioeconomic, environmental, and behavioral correlates have been analyzed thoroughly, the role played by population mobility on DENV epidemics, especially at the local level, remains scarce. This study assesses whether the daily pattern of population mobility is associated with DENV incidence in Campinas, a Brazilian major city with over 1.2 million inhabitants in São Paulo State. DENV notifications from 2007 to 2015 were geocoded at street level (n = 114,884) and combined with sociodemographic and environmental data from the 2010 population census. Population mobility was extracted from the Origin-Destination Survey (ODS), carried out in 2011, and daily precipitation was obtained from satellite imagery. Multivariate zero-inflated negative binomial regression models were applied. High population mobility presented a relevant positive effect on higher risk for DENV incidence. High income and residence in apartments were found to be protective characteristics against the disease, while unpaved streets, number of strategic points (such as scrapyards and tire repair shops), and precipitation were consistently risk factors.
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15
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Rader B, Scarpino SV, Nande A, Hill AL, Adlam B, Reiner RC, Pigott DM, Gutierrez B, Zarebski AE, Shrestha M, Brownstein JS, Castro MC, Dye C, Tian H, Pybus OG, Kraemer MUG. Crowding and the shape of COVID-19 epidemics. Nat Med 2020. [PMID: 33020651 DOI: 10.1101/2020.04.15.20064980] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic is straining public health systems worldwide, and major non-pharmaceutical interventions have been implemented to slow its spread1-4. During the initial phase of the outbreak, dissemination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was primarily determined by human mobility from Wuhan, China5,6. Yet empirical evidence on the effect of key geographic factors on local epidemic transmission is lacking7. In this study, we analyzed highly resolved spatial variables in cities, together with case count data, to investigate the role of climate, urbanization and variation in interventions. We show that the degree to which cases of COVID-19 are compressed into a short period of time (peakedness of the epidemic) is strongly shaped by population aggregation and heterogeneity, such that epidemics in crowded cities are more spread over time, and crowded cities have larger total attack rates than less populated cities. Observed differences in the peakedness of epidemics are consistent with a meta-population model of COVID-19 that explicitly accounts for spatial hierarchies. We paired our estimates with globally comprehensive data on human mobility and predict that crowded cities worldwide could experience more prolonged epidemics.
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Affiliation(s)
- Benjamin Rader
- Computational Epidemiology Lab, Boston Children's Hospital, Boston MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston MA, USA
| | - Samuel V Scarpino
- Network Science Institute, Northeastern University, Boston MA, USA.
- ISI Foundation, Turin, Italy.
- Santa Fe Institute, Santa Fe NM, USA.
| | - Anjalika Nande
- Program for Evolutionary Dynamics, Harvard University, Cambridge MA, USA
| | - Alison L Hill
- Program for Evolutionary Dynamics, Harvard University, Cambridge MA, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore MD, USA
| | - Ben Adlam
- Program for Evolutionary Dynamics, Harvard University, Cambridge MA, USA
| | - Robert C Reiner
- Department of Health Metrics, University of Washington, Seattle WA, USA
- Institute for Health Metrics and Evaluation, University of Washington, Seattle WA, USA
| | - David M Pigott
- Department of Health Metrics, University of Washington, Seattle WA, USA
- Institute for Health Metrics and Evaluation, University of Washington, Seattle WA, USA
| | - Bernardo Gutierrez
- Department of Zoology, University of Oxford, Oxford, UK
- School of Biological and Environmental Sciences, Universidad San Francisco de Quito USFQ, Quito, Ecuador
| | | | - Munik Shrestha
- Network Science Institute, Northeastern University, Boston MA, USA
| | - John S Brownstein
- Computational Epidemiology Lab, Boston Children's Hospital, Boston MA, USA
- Harvard Medical School, Boston MA, USA
| | - Marcia C Castro
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston MA, USA
| | | | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, UK.
- Department of Pathobiology and Population Science, The Royal Veterinary College, London, UK.
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16
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Abstract
The coronavirus disease 2019 (COVID-19) pandemic is straining public health systems worldwide, and major non-pharmaceutical interventions have been implemented to slow its spread1-4. During the initial phase of the outbreak, dissemination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was primarily determined by human mobility from Wuhan, China5,6. Yet empirical evidence on the effect of key geographic factors on local epidemic transmission is lacking7. In this study, we analyzed highly resolved spatial variables in cities, together with case count data, to investigate the role of climate, urbanization and variation in interventions. We show that the degree to which cases of COVID-19 are compressed into a short period of time (peakedness of the epidemic) is strongly shaped by population aggregation and heterogeneity, such that epidemics in crowded cities are more spread over time, and crowded cities have larger total attack rates than less populated cities. Observed differences in the peakedness of epidemics are consistent with a meta-population model of COVID-19 that explicitly accounts for spatial hierarchies. We paired our estimates with globally comprehensive data on human mobility and predict that crowded cities worldwide could experience more prolonged epidemics.
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17
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Walters M, Perkins TA. Hidden heterogeneity and its influence on dengue vaccination impact. Infect Dis Model 2020; 5:783-797. [PMID: 33102984 PMCID: PMC7558830 DOI: 10.1016/j.idm.2020.09.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 09/24/2020] [Indexed: 12/29/2022] Open
Abstract
The CYD-TDV vaccine was recently developed to combat dengue, a mosquito-borne viral disease that afflicts millions of people each year throughout the tropical and subtropical world. Its rollout has been complicated by recent findings that vaccinees with no prior exposure to dengue virus (DENV) experience an elevated risk of severe disease in response to their first DENV infection subsequent to vaccination. As a result of these findings, guidelines for use of CYD-TDV now require serological screening prior to vaccination to establish that an individual does not fall into this high-risk category. These complications mean that the public health impact of CYD-TDV vaccination is expected to be higher in areas with higher transmission. One important practical difficulty with tailoring vaccination policy to local transmission contexts is that DENV transmission is spatially heterogeneous, even at the scale of neighborhoods or blocks within a city. This raises the question of whether models based on data that average over spatial heterogeneity in transmission could fail to capture important aspects of CYD-TDV impact in spatially heterogeneous populations. We explored this question with a deterministic model of DENV transmission and CYD-TDV vaccination in a population comprised of two communities with differing transmission intensities. Compared to the full model, a version of the model based on the average of the two communities failed to capture benefits of targeting the intervention to the high-transmission community, which resulted in greater impact in both communities than we observed under even coverage. In addition, the model based on the average of the two communities substantially overestimated impact among vaccinated individuals in the low-transmission community. In the event that the specificity of serological screening is not high, this result suggests that models that ignore spatial heterogeneity could overlook the potential for harm to this segment of the population.
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Affiliation(s)
- Magdalene Walters
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - T Alex Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, 46556, USA
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18
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Wang Y, Xu C, Wu W, Ren J, Li Y, Gui L, Yao S. Time series analysis of temporal trends in hemorrhagic fever with renal syndrome morbidity rate in China from 2005 to 2019. Sci Rep 2020; 10:9609. [PMID: 32541833 PMCID: PMC7295973 DOI: 10.1038/s41598-020-66758-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Accepted: 05/26/2020] [Indexed: 12/04/2022] Open
Abstract
Hemorrhagic fever with renal syndrome (HFRS) is seriously endemic in China with 70%~90% of the notified cases worldwide and showing an epidemic tendency of upturn in recent years. Early detection for its future epidemic trends plays a pivotal role in combating this threat. In this scenario, our study investigates the suitability for application in analyzing and forecasting the epidemic tendencies based on the monthly HFRS morbidity data from 2005 through 2019 using the nonlinear model-based self-exciting threshold autoregressive (SETAR) and logistic smooth transition autoregressive (LSTAR) methods. The experimental results manifested that the SETAR and LSTAR approaches presented smaller values among the performance measures in both two forecasting subsamples, when compared with the most extensively used seasonal autoregressive integrated moving average (SARIMA) method, and the former slightly outperformed the latter. Descriptive statistics showed an epidemic tendency of downturn with average annual percent change (AAPC) of −5.640% in overall HFRS, however, an upward trend with an AAPC = 1.213% was observed since 2016 and according to the forecasts using the SETAR, it would seemingly experience an outbreak of HFRS in China in December 2019. Remarkably, there were dual-peak patterns in HFRS incidence with a strong one occurring in November until January of the following year, additionally, a weak one in May and June annually. Therefore, the SETAR and LSTAR approaches may be a potential useful tool in analyzing the temporal behaviors of HFRS in China.
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Affiliation(s)
- Yongbin Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, P.R. China.
| | - Chunjie Xu
- Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, P.R. China
| | - Weidong Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, P.R. China
| | - Jingchao Ren
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, P.R. China
| | - Yuchun Li
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, P.R. China
| | - Lihui Gui
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, P.R. China
| | - Sanqiao Yao
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, P.R. China
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19
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Wagner CE, Hooshyar M, Baker RE, Yang W, Arinaminpathy N, Vecchi G, Metcalf CJE, Porporato A, Grenfell BT. Climatological, virological and sociological drivers of current and projected dengue fever outbreak dynamics in Sri Lanka. J R Soc Interface 2020; 17:20200075. [PMID: 32486949 PMCID: PMC7328388 DOI: 10.1098/rsif.2020.0075] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 05/11/2020] [Indexed: 01/16/2023] Open
Abstract
The largest ever Sri Lankan dengue outbreak of 2017 provides an opportunity for investigating the relative contributions of climatological, epidemiological and sociological drivers on the epidemic patterns of this clinically important vector-borne disease. To do so, we develop a climatologically driven disease transmission framework for dengue virus using spatially resolved temperature and precipitation data as well as the time-series susceptible-infected-recovered (SIR) model. From this framework, we first demonstrate that the distinct climatological patterns encountered across the island play an important role in establishing the typical yearly temporal dynamics of dengue, but alone are unable to account for the epidemic case numbers observed in Sri Lanka during 2017. Using a simplified two-strain SIR model, we demonstrate that the re-introduction of a dengue virus serotype that had been largely absent from the island in previous years may have played an important role in driving the epidemic, and provide a discussion of the possible roles for extreme weather events and human mobility patterns on the outbreak dynamics. Lastly, we provide estimates for the future burden of dengue across Sri Lanka using the Coupled Model Intercomparison Phase 5 climate projections. Critically, we demonstrate that climatological and serological factors can act synergistically to yield greater projected case numbers than would be expected from the presence of a single driver alone. Altogether, this work provides a holistic framework for teasing apart and analysing the various complex drivers of vector-borne disease outbreak dynamics.
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Affiliation(s)
- Caroline E. Wagner
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
- Princeton Environmental Institute, Princeton University, Princeton, NJ 08544, USA
| | - Milad Hooshyar
- Princeton Environmental Institute, Princeton University, Princeton, NJ 08544, USA
- Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA
| | - Rachel E. Baker
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
- Princeton Environmental Institute, Princeton University, Princeton, NJ 08544, USA
| | - Wenchang Yang
- Princeton Environmental Institute, Princeton University, Princeton, NJ 08544, USA
- Department of Geosciences, Princeton University, Princeton, NJ 08544, USA
| | - Nimalan Arinaminpathy
- Department of Infectious Disease Epidemiology, Imperial College School of Medicine, London, UK
| | - Gabriel Vecchi
- Princeton Environmental Institute, Princeton University, Princeton, NJ 08544, USA
- Department of Geosciences, Princeton University, Princeton, NJ 08544, USA
| | - C. Jessica E. Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Amilcare Porporato
- Princeton Environmental Institute, Princeton University, Princeton, NJ 08544, USA
- Department of Geosciences, Princeton University, Princeton, NJ 08544, USA
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
- Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
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20
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Abdur Rehman N, Salje H, Kraemer MUG, Subramanian L, Saif U, Chunara R. Quantifying the localized relationship between vector containment activities and dengue incidence in a real-world setting: A spatial and time series modelling analysis based on geo-located data from Pakistan. PLoS Negl Trop Dis 2020; 14:e0008273. [PMID: 32392225 PMCID: PMC7241855 DOI: 10.1371/journal.pntd.0008273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 05/21/2020] [Accepted: 04/07/2020] [Indexed: 11/19/2022] Open
Abstract
Increasing urbanization is having a profound effect on infectious disease risk, posing significant challenges for governments to allocate limited resources for their optimal control at a sub-city scale. With recent advances in data collection practices, empirical evidence about the efficacy of highly localized containment and intervention activities, which can lead to optimal deployment of resources, is possible. However, there are several challenges in analyzing data from such real-world observational settings. Using data on 3.9 million instances of seven dengue vector containment activities collected between 2012 and 2017, here we develop and assess two frameworks for understanding how the generation of new dengue cases changes in space and time with respect to application of different types of containment activities. Accounting for the non-random deployment of each containment activity in relation to dengue cases and other types of containment activities, as well as deployment of activities in different epidemiological contexts, results from both frameworks reinforce existing knowledge about the efficacy of containment activities aimed at the adult phase of the mosquito lifecycle. Results show a 10% (95% CI: 1-19%) and 20% reduction (95% CI: 4-34%) reduction in probability of a case occurring in 50 meters and 30 days of cases which had Indoor Residual Spraying (IRS) and fogging performed in the immediate vicinity, respectively, compared to cases of similar epidemiological context and which had no containment in their vicinity. Simultaneously, limitations due to the real-world nature of activity deployment are used to guide recommendations for future deployment of resources during outbreaks as well as data collection practices. Conclusions from this study will enable more robust and comprehensive analyses of localized containment activities in resource-scarce urban settings and lead to improved allocation of resources of government in an outbreak setting.
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Affiliation(s)
- Nabeel Abdur Rehman
- Computer Science and Engineering, Tandon School of Engineering, New York University, Brooklyn, New York, United States of America
| | | | | | | | - Umar Saif
- UNESCO Chair for ICTD, Lahore, Pakistan
| | - Rumi Chunara
- Computer Science and Engineering, Tandon School of Engineering, New York University, Brooklyn, New York, United States of America
- Department of Biostatistics, School of Global Public Health, New York University, New York, New York, United States of America
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21
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Abd El Ghany M, Fouz N, Hill-Cawthorne GA. Human Movement and Transmission of Antimicrobial-Resistant Bacteria. THE HANDBOOK OF ENVIRONMENTAL CHEMISTRY 2020:311-344. [DOI: 10.1007/698_2020_560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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22
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Schaber KL, Paz-Soldan VA, Morrison AC, Elson WHD, Rothman AL, Mores CN, Astete-Vega H, Scott TW, Waller LA, Kitron U, Elder JP, Barker CM, Perkins TA, Vazquez-Prokopec GM. Dengue illness impacts daily human mobility patterns in Iquitos, Peru. PLoS Negl Trop Dis 2019; 13:e0007756. [PMID: 31545804 PMCID: PMC6776364 DOI: 10.1371/journal.pntd.0007756] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 10/03/2019] [Accepted: 09/05/2019] [Indexed: 11/25/2022] Open
Abstract
Background Human mobility plays a central role in shaping pathogen transmission by generating spatial and/or individual variability in potential pathogen-transmitting contacts. Recent research has shown that symptomatic infection can influence human mobility and pathogen transmission dynamics. Better understanding the complex relationship between symptom severity, infectiousness, and human mobility requires quantification of movement patterns throughout infectiousness. For dengue virus (DENV), human infectiousness peaks 0–2 days after symptom onset, making it paramount to understand human movement patterns from the beginning of illness. Methodology and principal findings Through community-based febrile surveillance and RT-PCR assays, we identified a cohort of DENV+ residents of the city of Iquitos, Peru (n = 63). Using retrospective interviews, we measured the movements of these individuals when healthy and during each day of symptomatic illness. The most dramatic changes in mobility occurred during the first three days after symptom onset; individuals visited significantly fewer locations (Wilcoxon test, p = 0.017) and spent significantly more time at home (Wilcoxon test, p = 0.005), compared to when healthy. By 7–9 days after symptom onset, mobility measures had returned to healthy levels. Throughout an individual’s symptomatic period, the day of illness and their subjective sense of well-being were the most significant predictors for the number of locations and houses they visited. Conclusions/Significance Our study is one of the first to collect and analyze human mobility data at a daily scale during symptomatic infection. Accounting for the observed changes in human mobility throughout illness will improve understanding of the impact of disease on DENV transmission dynamics and the interpretation of public health-based surveillance data. Dengue is the most important mosquito-borne viral disease of humans worldwide. Due to the limited mobility of the mosquitoes that transmit dengue virus, human mobility can be a key to both understanding an individual’s exposure to the virus and explaining the spread of dengue throughout a population. Accurate disease models should include human mobility; however, changes in human movement patterns due to the presence of symptoms need to be taken into account. We quantified the impact of symptom presence on human mobility throughout the infectious period by analyzing a dataset on the daily movements of dengue virus infected individuals. Accounting for these changing patterns of mobility will improve understanding of the complex relationship between symptom severity, human movement, and dengue virus transmission. Furthermore, dengue transmission models that incorporate symptom-driven mobility changes can be used to evaluate scenarios and strategies for disease prevention.
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Affiliation(s)
- Kathryn L. Schaber
- Program of Population Biology, Ecology and Evolution, Emory University, Atlanta, Georgia, United States of America
| | - Valerie A. Paz-Soldan
- Department of Global Community Health and Behavioral Sciences, Tulane School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
| | - Amy C. Morrison
- Department of Entomology and Nematology, University of California Davis, Davis, California, United States of America
| | - William H. D. Elson
- Department of Entomology and Nematology, University of California Davis, Davis, California, United States of America
| | - Alan L. Rothman
- Institute for Immunology and Informatics and Department of Cell and Molecular Biology, University of Rhode Island, Providence, Rhode Island, United States of America
| | - Christopher N. Mores
- Department of Virology and Emerging Infections, U.S. Naval Medical Research Unit No. 6, Lima and Iquitos, Peru
| | - Helvio Astete-Vega
- Department of Virology and Emerging Infections, U.S. Naval Medical Research Unit No. 6, Lima and Iquitos, Peru
| | - Thomas W. Scott
- Department of Entomology and Nematology, University of California Davis, Davis, California, United States of America
| | - Lance A. Waller
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Uriel Kitron
- Department of Environmental Sciences, Emory University, Atlanta, Georgia, United States of America
| | - John P. Elder
- Graduate School of Public Health, San Diego State University, San Diego, California, United States of America
| | - Christopher M. Barker
- Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, California, United States of America
| | - T. Alex Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Gonzalo M. Vazquez-Prokopec
- Program of Population Biology, Ecology and Evolution, Emory University, Atlanta, Georgia, United States of America
- Department of Environmental Sciences, Emory University, Atlanta, Georgia, United States of America
- * E-mail:
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23
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Harris M, Caldwell JM, Mordecai EA. Climate drives spatial variation in Zika epidemics in Latin America. Proc Biol Sci 2019; 286:20191578. [PMID: 31455188 PMCID: PMC6732388 DOI: 10.1098/rspb.2019.1578] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Between 2015 and 2017, Zika virus spread rapidly through populations in the Americas with no prior exposure to the disease. Although climate is a known determinant of many Aedes-transmitted diseases, it is currently unclear whether climate was a major driver of the Zika epidemic and how climate might have differentially impacted outbreak intensity across locations within Latin America. Here, we estimated force of infection for Zika over time and across provinces in Latin America using a time-varying susceptible–infectious–recovered model. Climate factors explained less than 5% of the variation in weekly transmission intensity in a spatio-temporal model of force of infection by province over time, suggesting that week to week transmission within provinces may be too stochastic to predict. By contrast, climate and population factors were highly predictive of spatial variation in the presence and intensity of Zika transmission among provinces, with pseudo-R2 values between 0.33 and 0.60. Temperature, temperature range, rainfall and population size were the most important predictors of where Zika transmission occurred, while rainfall, relative humidity and a nonlinear effect of temperature were the best predictors of Zika intensity and burden. Surprisingly, force of infection was greatest in locations with temperatures near 24°C, much lower than previous estimates from mechanistic models, potentially suggesting that existing vector control programmes and/or prior exposure to other mosquito-borne diseases may have limited transmission in locations most suitable for Aedes aegypti, the main vector of Zika, dengue and chikungunya viruses in Latin America.
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Affiliation(s)
- Mallory Harris
- Odum School of Ecology, University of Georgia, 140 E Green St, Athens, GA 30602, USA
| | - Jamie M Caldwell
- Biology Department, Stanford University, 371 Serra Mall, Stanford, CA, USA
| | - Erin A Mordecai
- Biology Department, Stanford University, 371 Serra Mall, Stanford, CA, USA
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24
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Obolski U, Perez PN, Villabona‐Arenas CJ, Thézé J, Faria NR, Lourenço J. MVSE: An R-package that estimates a climate-driven mosquito-borne viral suitability index. Methods Ecol Evol 2019; 10:1357-1370. [PMID: 32391139 PMCID: PMC7202302 DOI: 10.1111/2041-210x.13205] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 04/23/2019] [Indexed: 12/05/2022]
Abstract
Viruses, such as dengue, Zika, yellow fever and chikungunya, depend on mosquitoes for transmission. Their epidemics typically present periodic patterns, linked to the underlying mosquito population dynamics, which are known to be driven by natural climate fluctuations. Understanding how climate dictates the timing and potential of viral transmission is essential for preparedness of public health systems and design of control strategies. While various alternative approaches have been proposed to estimate local transmission potential of such viruses, few open-source, ready to use and freely available software tools exist.We developed the Mosquito-borne Viral Suitability Estimator (MVSE) software package for the R programming environment. MVSE estimates the index P, a novel suitability index based on a climate-driven mathematical expression for the basic reproductive number of mosquito-borne viruses. By accounting for local humidity and temperature, as well as viral, vector and human priors, the index P can be estimated for specific host and viral species in different regions of the globe.We describe the background theory, empirical support and biological interpretation of the index P. Using real-world examples spanning multiple epidemiological contexts, we further demonstrate MVSE's basic functionality, research and educational potentials.
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Affiliation(s)
- Uri Obolski
- School of Public HealthTel Aviv UniversityTel AvivIsrael
- Porter School of the Environment and Earth SciencesTel Aviv UniversityTel AvivIsrael
| | - Pablo N. Perez
- Department of Infectious Disease EpidemiologyImperial College LondonLondonUK
| | - Christian J. Villabona‐Arenas
- Centre for Mathematical Modelling of Infectious DiseasesDepartment of Infectious Disease EpidemiologyFaculty of Epidemiology and Population Health, LondonSchool of Hygiene and Tropical MedicineLondonUK
| | - Julien Thézé
- Department of ZoologyUniversity of OxfordOxfordUK
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25
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Abdullah, Ali S, Salman M, Din M, Khan K, Ahmad M, Khan FH, Arif M. Dengue Outbreaks in Khyber Pakhtunkhwa (KPK), Pakistan in 2017: An Integrated Disease Surveillance and Response System (IDSRS)-Based Report. Pol J Microbiol 2019; 68:115-119. [PMID: 31050259 PMCID: PMC7256837 DOI: 10.21307/pjm-2019-013] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/17/2018] [Indexed: 12/26/2022] Open
Abstract
The current study is a retrospective epidemic report regarding dengue fever (DF) virus infection cases (2017) from fifteen districts of KPK, Pakistan. Medical records of 120 948 patients were reviewed retrospectively for demographic, clinical and laboratory data. The presence of dengue infection was confirmed by NS1-ELISA and RT-PCR, respectively. The total positive cases (of suspected DF samples) were 24 938 (20.6%), whereas seventy cases (0.28%) had a fatal outcome. Mean age ± SD of the dengue patients was 26 ± 19.8 years, while; the most affected age group was from 16 to 30 years (Chi-square: 12 820.125, p: 0.00). The infected males were 65.3%, and that of the female was 34.7%. All the dengue-infected patients were observed with symptoms of severe fever (100%), body aches (95%), gums and nose bleeding (5%), skin rashes (30%), vomiting (70%). The highest infection rate was found in district Peshawar and that of the lowest was in Bannu, Hungu and Luki Marwat. A high rate of dengue infection was found in post-monsoon months i.e. October (41%) and September (32%) of the year. The results proved that if the dengue outbreaks reveal further in KPK, it could alarmingly increase the mortality rate. Therefore, the Department of Public Health in KPK, Pakistan may take proper measures to avoid and control dengue epidemics in the future.
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Affiliation(s)
- Abdullah
- Department of Biotechnology, Abdul Wali University , Mardan , Pakistan
| | - Sher Ali
- Universidade Federal do Paraná Brazil, Department of Chemistry , Parana , Brazil
| | - Muhammad Salman
- Department of Microbiology and Biotechnology, Abasyn University Peshawar , Pakistan
| | - Misbahud Din
- Department of Biotechnology, Abdul Wali University , Mardan , Pakistan
| | - Kachkol Khan
- Tehsil Head Quarter Hospital Dargai , Malakand , Pakistan
| | - Munib Ahmad
- Department of Biotechnology, Abdul Wali University , Mardan , Pakistan
| | - Faisal Hayat Khan
- Department of Biotechnology, Abdul Wali University , Mardan , Pakistan
| | - Muhammad Arif
- Department of Biotechnology, Abdul Wali University , Mardan , Pakistan
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26
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Kraemer MUG, Reiner RC, Brady OJ, Messina JP, Gilbert M, Pigott DM, Yi D, Johnson K, Earl L, Marczak LB, Shirude S, Davis Weaver N, Bisanzio D, Perkins TA, Lai S, Lu X, Jones P, Coelho GE, Carvalho RG, Van Bortel W, Marsboom C, Hendrickx G, Schaffner F, Moore CG, Nax HH, Bengtsson L, Wetter E, Tatem AJ, Brownstein JS, Smith DL, Lambrechts L, Cauchemez S, Linard C, Faria NR, Pybus OG, Scott TW, Liu Q, Yu H, Wint GRW, Hay SI, Golding N. Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus. Nat Microbiol 2019; 4:854-863. [PMID: 30833735 PMCID: PMC6522366 DOI: 10.1038/s41564-019-0376-y] [Citation(s) in RCA: 567] [Impact Index Per Article: 113.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 01/18/2019] [Indexed: 12/20/2022]
Abstract
The global population at risk from mosquito-borne diseases-including dengue, yellow fever, chikungunya and Zika-is expanding in concert with changes in the distribution of two key vectors: Aedes aegypti and Aedes albopictus. The distribution of these species is largely driven by both human movement and the presence of suitable climate. Using statistical mapping techniques, we show that human movement patterns explain the spread of both species in Europe and the United States following their introduction. We find that the spread of Ae. aegypti is characterized by long distance importations, while Ae. albopictus has expanded more along the fringes of its distribution. We describe these processes and predict the future distributions of both species in response to accelerating urbanization, connectivity and climate change. Global surveillance and control efforts that aim to mitigate the spread of chikungunya, dengue, yellow fever and Zika viruses must consider the so far unabated spread of these mosquitos. Our maps and predictions offer an opportunity to strategically target surveillance and control programmes and thereby augment efforts to reduce arbovirus burden in human populations globally.
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Affiliation(s)
- Moritz U G Kraemer
- Department of Zoology, University of Oxford, Oxford, UK.
- Harvard Medical School, Harvard University, Boston, MA, USA.
- Boston Children's Hospital, Boston, MA, USA.
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Oliver J Brady
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Jane P Messina
- School of Geography and the Environment, University of Oxford, Oxford, UK
- Oxford School of Global and Area Studies, University of Oxford, Oxford, UK
| | - Marius Gilbert
- Spatial Epidemiology Lab (SpELL), Universite Libre de Bruxelles, Brussels, Belgium
- Fonds National de la Recherche Scientifique, Brussels, Belgium
| | - David M Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Dingdong Yi
- Department of Statistics, Harvard University, Cambridge, MA, USA
| | - Kimberly Johnson
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Lucas Earl
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Laurie B Marczak
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Shreya Shirude
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Nicole Davis Weaver
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Donal Bisanzio
- RTI International, Washington, DC, USA
- Epidemiology and Public Health Division, School of Medicine, University of Nottingham, Nottingham, UK
| | - T Alex Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - Shengjie Lai
- School of Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
- Department of Geography and Environment, University of Southampton, Southampton, UK
- Flowminder Foundation, Stockholm, Sweden
| | - Xin Lu
- School of Business, Central South University, Changsha, China
- College of Systems Engineering, National University of Defense Technology, Changsha, China
- School of Business Administration, Southwestern University of Finance and Economics, Chengdu, China
| | - Peter Jones
- Waen Associates Ltd, Y Waen, Islaw'r Dref, Dolgellau, Gwynedd, UK
| | | | | | - Wim Van Bortel
- European Centre for Disease Prevention and Control, Stockholm, Sweden
- Institute of Tropical Medicine, Antwerp, Belgium
| | | | | | | | - Chester G Moore
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO, USA
| | - Heinrich H Nax
- Computational Social Science, ETH Zurich, Zurich, Switzerland
| | - Linus Bengtsson
- Flowminder Foundation, Stockholm, Sweden
- Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Erik Wetter
- Flowminder Foundation, Stockholm, Sweden
- Stockholm School of Economics, Stockholm, Sweden
| | - Andrew J Tatem
- Department of Geography and Environment, University of Southampton, Southampton, UK
- Flowminder Foundation, Stockholm, Sweden
| | - John S Brownstein
- Harvard Medical School, Harvard University, Boston, MA, USA
- Boston Children's Hospital, Boston, MA, USA
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Louis Lambrechts
- Insect-Virus Interactions Unit, Institut Pasteur, CNRS, UMR2000, Paris, France
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, CNRS, UMR2000, Paris, France
| | - Catherine Linard
- Spatial Epidemiology Lab (SpELL), Universite Libre de Bruxelles, Brussels, Belgium
- Department of Geography, Universite de Namur, Namur, Belgium
| | - Nuno R Faria
- Department of Zoology, University of Oxford, Oxford, UK
| | | | - Thomas W Scott
- Department of Entomology and Nematology, University of California, Davis, Davis, CA, USA
| | - Qiyong Liu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, China
- Shandong University Climate Change and Health Center, School of Public Health, Shandong University, Jinan, Shandong, China
- WHO Collaborating Centre for Vector Surveillance and Management, Beijing, China
- Chongqing Centre for Disease Control and Prevention, Chongqing, China
| | - Hongjie Yu
- School of Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - G R William Wint
- Department of Zoology, University of Oxford, Oxford, UK
- Environmental Research Group Oxford (ERGO), Department of Zoology, Oxford University, Oxford, UK
| | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
| | - Nick Golding
- School of BioSciences, University of Melbourne, Parkville, Victoria, Australia.
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27
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Kraemer MUG, Golding N, Bisanzio D, Bhatt S, Pigott DM, Ray SE, Brady OJ, Brownstein JS, Faria NR, Cummings DAT, Pybus OG, Smith DL, Tatem AJ, Hay SI, Reiner RC. Utilizing general human movement models to predict the spread of emerging infectious diseases in resource poor settings. Sci Rep 2019; 9:5151. [PMID: 30914669 PMCID: PMC6435716 DOI: 10.1038/s41598-019-41192-3] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 03/03/2019] [Indexed: 12/03/2022] Open
Abstract
Human mobility is an important driver of geographic spread of infectious pathogens. Detailed information about human movements during outbreaks are, however, difficult to obtain and may not be available during future epidemics. The Ebola virus disease (EVD) outbreak in West Africa between 2014-16 demonstrated how quickly pathogens can spread to large urban centers following one cross-species transmission event. Here we describe a flexible transmission model to test the utility of generalised human movement models in estimating EVD cases and spatial spread over the course of the outbreak. A transmission model that includes a general model of human mobility significantly improves prediction of EVD's incidence compared to models without this component. Human movement plays an important role not only to ignite the epidemic in locations previously disease free, but over the course of the entire epidemic. We also demonstrate important differences between countries in population mixing and the improved prediction attributable to movement metrics. Given their relative rareness, locally derived mobility data are unlikely to exist in advance of future epidemics or pandemics. Our findings show that transmission patterns derived from general human movement models can improve forecasts of spatio-temporal transmission patterns in places where local mobility data is unavailable.
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Affiliation(s)
- M U G Kraemer
- Department of Zoology, University of Oxford, Oxford, UK.
- Harvard Medical School, Boston, MA, USA.
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA.
| | - N Golding
- Department of BioSciences, University of Melbourne, Parkville, VIC, Australia
| | - D Bisanzio
- RTI International, Washington, D.C., USA
- Epidemiology and Public Health Division, School of Medicine, University of Nottingham, Nottingham, UK
| | - S Bhatt
- Imperial College London, London, United Kingdom
| | - D M Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - S E Ray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - O J Brady
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - J S Brownstein
- Harvard Medical School, Boston, MA, USA
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA
| | - N R Faria
- Department of Zoology, University of Oxford, Oxford, UK
| | - D A T Cummings
- Department of Biology, University of Florida, Gainesville, FL, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - O G Pybus
- Department of Zoology, University of Oxford, Oxford, UK
| | - D L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Sanaria Institute for Global Health and Tropical Medicine, Rockville, USA
| | - A J Tatem
- WorldPop, Department of Geography and Environmental Sciences, University of Southampton, Southampton, UK
- Flowminder Foundation, Stockholm, Sweden
| | - S I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
| | - R C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
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28
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Mahmood S, Irshad A, Nasir JM, Sharif F, Farooqi SH. Spatiotemporal analysis of dengue outbreaks in Samanabad town, Lahore metropolitan area, using geospatial techniques. ENVIRONMENTAL MONITORING AND ASSESSMENT 2019; 191:55. [PMID: 30617862 DOI: 10.1007/s10661-018-7162-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 12/13/2018] [Indexed: 06/09/2023]
Abstract
Dengue is endemic to Pakistan with its usual peak incidence in the post-monsoon period. In the last decade, dengue outbreaks have occurred in major urban areas particularly Karachi and Lahore, affecting large numbers of people. This study is an attempt to analyze the spatiotemporal variation of dengue fever (DF) in Samanabad town, Lahore metropolitan area. The study is based on secondary data, acquired from concerned government departments. Point level geo-coding is used to transform the relative location to the absolute location using Google Earth, and Global Position System (GPS) is used to validate the geo-coded location. Geographic information system (GIS) has been used to perform spatial analysis. It has been found that temporally DF prevalence varies from month to month and year to year. Major outbreak was observed in the year 2013 with more than 900 confirmed DF cases. Rainfall, temperature, and humidity have played a central role in outbreaks. The land cover pattern and population density further intensified the outbreak. Spatially, the number of DF incidence was high in those localities where the entire land is built-up and with little/no green space areas. Analysis reveals that DF is still a major threat to the area as socioeconomic and geographic conditions favor vector breeding and transfer of disease from one person/place to another. This study presents useful information regarding spatiotemporal patterns of dengue outbreak and may bring the attention of public health departments to formulate dengue-combating strategies. The methodology is general for spatiotemporal analysis and can be applied to other infectious diseases as well.
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Affiliation(s)
- Shakeel Mahmood
- Department of Geography, Government College University Lahore, Lower Mall, District Lahore, Lahore, 54000, Pakistan.
| | - Ahtisham Irshad
- Department of Geography, Government College University Lahore, Lower Mall, District Lahore, Lahore, 54000, Pakistan
| | | | - Faiza Sharif
- Sustainable Development Study Center, Government College University Lahore, Lahore, Pakistan
| | - Shahid Hussain Farooqi
- Department of Clinical Medicine and Surgery, University of Veterinary and Animal Sciences, Lahore, 54000, Pakistan
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Gallagher ME, Brooke CB, Ke R, Koelle K. Causes and Consequences of Spatial Within-Host Viral Spread. Viruses 2018; 10:E627. [PMID: 30428545 PMCID: PMC6267451 DOI: 10.3390/v10110627] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 11/08/2018] [Accepted: 11/10/2018] [Indexed: 02/07/2023] Open
Abstract
The spread of viral pathogens both between and within hosts is inherently a spatial process. While the spatial aspects of viral spread at the epidemiological level have been increasingly well characterized, the spatial aspects of viral spread within infected hosts are still understudied. Here, with a focus on influenza A viruses (IAVs), we first review experimental studies that have shed light on the mechanisms and spatial dynamics of viral spread within hosts. These studies provide strong empirical evidence for highly localized IAV spread within hosts. Since mathematical and computational within-host models have been increasingly used to gain a quantitative understanding of observed viral dynamic patterns, we then review the (relatively few) computational modeling studies that have shed light on possible factors that structure the dynamics of spatial within-host IAV spread. These factors include the dispersal distance of virions, the localization of the immune response, and heterogeneity in host cell phenotypes across the respiratory tract. While informative, we find in these studies a striking absence of theoretical expectations of how spatial dynamics may impact the dynamics of viral populations. To mitigate this, we turn to the extensive ecological and evolutionary literature on range expansions to provide informed theoretical expectations. We find that factors such as the type of density dependence, the frequency of long-distance dispersal, specific life history characteristics, and the extent of spatial heterogeneity are critical factors affecting the speed of population spread and the genetic composition of spatially expanding populations. For each factor that we identified in the theoretical literature, we draw parallels to its analog in viral populations. We end by discussing current knowledge gaps related to the spatial component of within-host IAV spread and the potential for within-host spatial considerations to inform the development of disease control strategies.
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Affiliation(s)
| | - Christopher B Brooke
- Department of Microbiology, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA.
| | - Ruian Ke
- T-6, Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
| | - Katia Koelle
- Department of Biology, Emory University, Atlanta, GA 30322, USA.
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Kraemer MUG, Cummings DAT, Funk S, Reiner RC, Faria NR, Pybus OG, Cauchemez S. Reconstruction and prediction of viral disease epidemics. Epidemiol Infect 2018; 147:e34. [PMID: 30394230 PMCID: PMC6398585 DOI: 10.1017/s0950268818002881] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 08/24/2018] [Accepted: 09/21/2018] [Indexed: 01/29/2023] Open
Abstract
A growing number of infectious pathogens are spreading among geographic regions. Some pathogens that were previously not considered to pose a general threat to human health have emerged at regional and global scales, such as Zika and Ebola Virus Disease. Other pathogens, such as yellow fever virus, were previously thought to be under control but have recently re-emerged, causing new challenges to public health organisations. A wide array of new modelling techniques, aided by increased computing capabilities, novel diagnostic tools, and the increased speed and availability of genomic sequencing allow researchers to identify new pathogens more rapidly, assess the likelihood of geographic spread, and quantify the speed of human-to-human transmission. Despite some initial successes in predicting the spread of acute viral infections, the practicalities and sustainability of such approaches will need to be evaluated in the context of public health responses.
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Affiliation(s)
- M. U. G. Kraemer
- Harvard Medical School, Harvard University, Boston, MA, USA
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA
- Department of Zoology, University of Oxford, Oxford, UK
| | - D. A. T. Cummings
- Department of Biology, University of Florida, Gainesville, Florida, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
| | - S. Funk
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - R. C. Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA
| | - N. R. Faria
- Department of Zoology, University of Oxford, Oxford, UK
| | - O. G. Pybus
- Department of Zoology, University of Oxford, Oxford, UK
| | - S. Cauchemez
- Mathematical Modelling of Infectious Diseases and Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France
- CNRS UMR2000: Génomique évolutive, modélisation et santé, Paris, France
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31
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Perez-Guzman PN, Carlos Junior Alcantara L, Obolski U, de Lima MM, Ashley EA, Smithuis F, Horby P, Maude RJ, Lin Z, Kyaw AMM, Lourenço J. Measuring Mosquito-borne Viral Suitability in Myanmar and Implications for Local Zika Virus Transmission. PLOS CURRENTS 2018; 10:ecurrents.outbreaks.7a6c64436a3085ebba37e5329ba169e6. [PMID: 31032144 PMCID: PMC6472868 DOI: 10.1371/currents.outbreaks.7a6c64436a3085ebba37e5329ba169e6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
INTRODUCTION In South East Asia, mosquito-borne viruses (MBVs) have long been a cause of high disease burden and significant economic costs. While in some SEA countries the epidemiology of MBVs is spatio-temporally well characterised and understood, in others such as Myanmar our understanding is largely incomplete. MATERIALS AND METHODS Here, we use a simple mathematical approach to estimate a climate-driven suitability index aiming to better characterise the intrinsic, spatio-temporal potential of MBVs in Myanmar. RESULTS Results show that the timing and amplitude of the natural oscillations of our suitability index are highly informative for the temporal patterns of DENV case counts at the country level, and a mosquito-abundance measure at a city level. When projected at fine spatial scales, the suitability index suggests that the time period of highest MBV transmission potential is between June and October independently of geographical location. Higher potential is nonetheless found along the middle axis of the country and in particular in the southern corridor of international borders with Thailand. DISCUSSION This research complements and expands our current understanding of MBV transmission potential in Myanmar, by identifying key spatial heterogeneities and temporal windows of importance for surveillance and control. We discuss our findings in the context of Zika virus given its recent worldwide emergence, public health impact, and current lack of information on its epidemiology and transmission potential in Myanmar. The proposed suitability index here demonstrated is applicable to other regions of the world for which surveillance data is missing, either due to lack of resources or absence of an MBV of interest.
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Affiliation(s)
- Pablo Noel Perez-Guzman
- Department of Global Health and Tropical Medicine, University of Oxford, UK; Department of Infectious Disease Epidemiology, Imperial College, London, UK
| | | | - Uri Obolski
- Department of Zoology, University of Oxford, UK
| | - Maricelia M de Lima
- Laboratory of Haematology, Genetics and Computational Biology, FIOCRUZ, Brazil
| | - Elizabeth A Ashley
- Myanmar-Oxford Clinical Research Unit, Yangon; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, UK
| | - Frank Smithuis
- Myanmar-Oxford Clinical Research Unit, Yangon; Nuffield Department of Medicine, University of Oxford, UK; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, UK
| | - Peter Horby
- Nuffield Department of Medicine, University of Oxford, UK
| | - Richard J Maude
- Nuffield Department of Medicine, University of Oxford, UK; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, UK; Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University,Thailand; Harvard TH Chan School of Public Health, Harvard University, Boston, USA
| | - Zaw Lin
- Myanmar Ministry of Health and Sports, Naypyidaw, Myanmar
| | | | - José Lourenço
- Department of Zoology, University of Oxford, Oxford, UK
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32
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Moore SM, Ten Bosch QA, Siraj AS, Soda KJ, España G, Campo A, Gómez S, Salas D, Raybaud B, Wenger E, Welkhoff P, Perkins TA. Local and regional dynamics of chikungunya virus transmission in Colombia: the role of mismatched spatial heterogeneity. BMC Med 2018; 16:152. [PMID: 30157921 PMCID: PMC6116375 DOI: 10.1186/s12916-018-1127-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 07/12/2018] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Mathematical models of transmission dynamics are routinely fitted to epidemiological time series, which must inevitably be aggregated at some spatial scale. Weekly case reports of chikungunya have been made available nationally for numerous countries in the Western Hemisphere since late 2013, and numerous models have made use of this data set for forecasting and inferential purposes. Motivated by an abundance of literature suggesting that the transmission of this mosquito-borne pathogen is localized at scales much finer than nationally, we fitted models at three different spatial scales to weekly case reports from Colombia to explore limitations of analyses of nationally aggregated time series data. METHODS We adapted the recently developed Disease Transmission Kernel (DTK)-Dengue model for modeling chikungunya virus (CHIKV) transmission, given the numerous similarities of these viruses vectored by a common mosquito vector. We fitted versions of this model specified at different spatial scales to weekly case reports aggregated at different spatial scales: (1) single-patch national model fitted to national data; (2) single-patch departmental models fitted to departmental data; and (3) multi-patch departmental models fitted to departmental data, where the multiple patches refer to municipalities within a department. We compared the consistency of simulations from fitted models with empirical data. RESULTS We found that model consistency with epidemic dynamics improved with increasing spatial granularity of the model. Specifically, the sum of single-patch departmental model fits better captured national-level temporal patterns than did a single-patch national model. Likewise, multi-patch departmental model fits better captured department-level temporal patterns than did single-patch departmental model fits. Furthermore, inferences about municipal-level incidence based on multi-patch departmental models fitted to department-level data were positively correlated with municipal-level data that were withheld from model fitting. CONCLUSIONS Our model performed better when posed at finer spatial scales, due to better matching between human populations with locally relevant risk. Confronting spatially aggregated models with spatially aggregated data imposes a serious structural constraint on model behavior by averaging over epidemiologically meaningful spatial variation in drivers of transmission, impairing the ability of models to reproduce empirical patterns.
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Affiliation(s)
- Sean M Moore
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA.
| | - Quirine A Ten Bosch
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, 75015, Paris, France
- CNRS UMR2000: Génomique évolutive, modélisation et santé (GEMS), Institut Pasteur, Paris, France
- Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, 75015, Paris, France
| | - Amir S Siraj
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - K James Soda
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - Guido España
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - Alfonso Campo
- Subdirección de Análisis de Riesgo y Respuesta Inmediata en Salud Pública, Instituto Nacional de Salud de Colombia, Bogotá, Colombia
| | - Sara Gómez
- Grupo de Enfermedades Transmisibles, Instituto Nacional de Salud de Colombia, Bogotá, Colombia
| | - Daniela Salas
- Grupo de Enfermedades Transmisibles, Instituto Nacional de Salud de Colombia, Bogotá, Colombia
| | | | | | | | - T Alex Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA.
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Comprehensive evaluation of demographic, socio-economic and other associated risk factors affecting the occurrence of dengue incidence among Colombo and Kandy Districts of Sri Lanka: a cross-sectional study. Parasit Vectors 2018; 11:478. [PMID: 30143051 PMCID: PMC6109346 DOI: 10.1186/s13071-018-3060-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 08/13/2018] [Indexed: 11/11/2022] Open
Abstract
Background Comprehensive understanding of risk factors related to socio-economic and demographic status and knowledge, attitudes and practices (KAP) of local communities play a key role in the design and implementation of community-based vector management programmes, along with the identification of gaps in existing control activities. Methods A total of 10 Medical Officers of Health (MOH) areas recording high dengue incidence over the last five years were selected from Colombo (n = 5) and Kandy (n = 5) Districts, Sri Lanka. From each MOH area, 200 houses reporting past dengue incidence were selected randomly as test group (n = 1000 for each district) based on the dengue case records available at relevant MOH offices. Information on socio-economic and demographic status and knowledge, attitudes and practices were gathered using an interviewer administered questionnaire. The control group contained 200 households from each MOH area that had not reported any dengue case and the same questionnaire was used for the assessment (n = 1000 for each district). Statistical comparisons between the test and control groups were carried out using the Chi-square test of independence, cluster analysis, analysis of similarities (ANOSIM) and multi-dimensional scaling (MDS) analysis. Results Significant differences among the test and control groups in terms of basic demographic and socio-economic factors, living standards, knowledge, attitude and practices, were recognized (P < 0.05 at 95% level of confidence). The test group indicated similar risk factors, while the control group also shared more or less similar characteristics as depicted by the findings of cluster analysis and ANOSIM. Findings of the present study highlight the importance of further improvement in community education, motivation and communication gaps, proper coordination and integration of control programmes with relevant entities. Key infrastructural risk factors such as urbanization and waste collection, should be further improved, while vector controlling entities should focus more on the actual conditions represented by the public on knowledge, attitudes and personal protective practices. Conclusions The design of flexible and community friendly intervention programmes to ensure the efficacy and sustainability of controlling dengue vectors through community based integrated vector management strategies, is recommended.
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34
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Romeo-Aznar V, Paul R, Telle O, Pascual M. Mosquito-borne transmission in urban landscapes: the missing link between vector abundance and human density. Proc Biol Sci 2018; 285:rspb.2018.0826. [PMID: 30111594 PMCID: PMC6111166 DOI: 10.1098/rspb.2018.0826] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 07/17/2018] [Indexed: 12/25/2022] Open
Abstract
With escalating urbanization, the environmental, demographic, and socio-economic heterogeneity of urban landscapes poses a challenge to mathematical models for the transmission of vector-borne infections. Classical coupled vector–human models typically assume that mosquito abundance is either independent from, or proportional to, human population density, implying a decreasing force of infection, or per capita infection rate with host number. We question these assumptions by introducing an explicit dependence between host and vector densities through different recruitment functions, whose dynamical consequences we examine in a modified model formulation. Contrasting patterns in the force of infection are demonstrated, including in particular increasing trends when recruitment grows sufficiently fast with human density. Interaction of these patterns with seasonality in temperature can give rise to pronounced differences in timing, relative peak sizes, and duration of epidemics. These proposed dependencies explain empirical dengue risk patterns observed in the city of Delhi where socio-economic status has an impact on both human and mosquito densities. These observed risk trends with host density are inconsistent with current standard models. A better understanding of the connection between vector recruitment and host density is needed to address the population dynamics of mosquito-transmitted infections in urban landscapes.
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Affiliation(s)
- Victoria Romeo-Aznar
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA
| | - Richard Paul
- Institut Pasteur, Functional Genetics of Infectious Diseases Unit, 75724 Paris Cedex 15, France.,Centre National de la Recherche Scientifique (CNRS), Génomique évolutive, modélisation et santé UMR 2000, 75724 Paris Cedex 15, France
| | - Olivier Telle
- Centre National de la Recherche Scientifique (CNRS), Centre de Sciences Humaines (CSH), Delhi, India.,Center for Policy Research (CPR), Delhi, India
| | - Mercedes Pascual
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA .,Santa Fe Institute, Santa Fe, NM, 87501, USA
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35
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Zhu G, Xiao J, Zhang B, Liu T, Lin H, Li X, Song T, Zhang Y, Ma W, Hao Y. The spatiotemporal transmission of dengue and its driving mechanism: A case study on the 2014 dengue outbreak in Guangdong, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 622-623:252-259. [PMID: 29216466 DOI: 10.1016/j.scitotenv.2017.11.314] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Revised: 11/15/2017] [Accepted: 11/27/2017] [Indexed: 05/19/2023]
Abstract
Dengue transmission is a complex spatiotemporal process with hidden interactions between hosts, vectors, and viruses as well as environment. This study aims to identify the transmission patterns and the driving mechanism that contributed to the dengue epidemics occurred in Guangdong Province of China in 2014. Based on the city-specific epidemiological, meteorological, demographic and geographic data, we first performed wavelet analysis and then integrated the key dynamics (i.e., mosquito population dynamics, human movement, virus transmission, and parameter estimation) into a transmission model. Using these methods, we found a clear temporal sequence and correlation of dengue transmission between cities, and such relationship is associated with socioeconomic factors. We further obtained the specific component of dengue incidence data in each city, and presented the underlying infectivity networks for characterizing how dengue transmits from one location to another. The results showed that the communication of in-out infections with Guangzhou and Foshan could be responsible for the large-scale diffusion of dengue epidemics in Guangdong in 2014. Our findings can offer new insights into how to improve the predictability and risk assessment of dengue transmission.
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Affiliation(s)
- Guanghu Zhu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China; Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Bing Zhang
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Tao Liu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Hualiang Lin
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Xing Li
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Tie Song
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Yonghui Zhang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Wenjun Ma
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China.
| | - Yuantao Hao
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
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36
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Siraj AS, Rodriguez-Barraquer I, Barker CM, Tejedor-Garavito N, Harding D, Lorton C, Lukacevic D, Oates G, Espana G, Kraemer MUG, Manore C, Johansson MA, Tatem AJ, Reiner RC, Perkins TA. Spatiotemporal incidence of Zika and associated environmental drivers for the 2015-2016 epidemic in Colombia. Sci Data 2018; 5:180073. [PMID: 29688216 PMCID: PMC5914286 DOI: 10.1038/sdata.2018.73] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Accepted: 03/19/2018] [Indexed: 11/14/2022] Open
Abstract
Despite a long history of mosquito-borne virus epidemics in the Americas, the impact of the Zika virus (ZIKV) epidemic of 2015–2016 was unexpected. The need for scientifically informed decision-making is driving research to understand the emergence and spread of ZIKV. To support that research, we assembled a data set of key covariates for modeling ZIKV transmission dynamics in Colombia, where ZIKV transmission was widespread and the government made incidence data publically available. On a weekly basis between January 1, 2014 and October 1, 2016 at three administrative levels, we collated spatiotemporal Zika incidence data, nine environmental variables, and demographic data into a single downloadable database. These new datasets and those we identified, processed, and assembled at comparable spatial and temporal resolutions will save future researchers considerable time and effort in performing these data processing steps, enabling them to focus instead on extracting epidemiological insights from this important data set. Similar approaches could prove useful for filling data gaps to enable epidemiological analyses of future disease emergence events.
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Affiliation(s)
- Amir S Siraj
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, 345 Galvin Hall, Notre Dame, IN 46556, USA
| | - Isabel Rodriguez-Barraquer
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD 21205, USA
| | - Christopher M Barker
- Department of Pathology, Microbiology and Immunology, University of California, 5329 Vet Med 3A, Davis, CA 95616, USA
| | - Natalia Tejedor-Garavito
- WorldPop, Department of Geography and Environment, University of Southampton, University Road, Southampton, SO17 1BJ, UK.,Flowminder Foundation, Roslagsgatan 17, SE-11355, Stockholm, Sweden
| | - Dennis Harding
- Institute for Disease Modeling, Bellevue, 3150 139th Ave SE, WA 98005, USA
| | - Christopher Lorton
- Institute for Disease Modeling, Bellevue, 3150 139th Ave SE, WA 98005, USA
| | - Dejan Lukacevic
- Institute for Disease Modeling, Bellevue, 3150 139th Ave SE, WA 98005, USA
| | - Gene Oates
- Institute for Disease Modeling, Bellevue, 3150 139th Ave SE, WA 98005, USA
| | - Guido Espana
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, 345 Galvin Hall, Notre Dame, IN 46556, USA
| | - Moritz U G Kraemer
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK.,Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA.,Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA
| | - Carrie Manore
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Michael A Johansson
- Centers for Disease Control and Prevention, 1324 Calle Canada, San Juan, PR 00920-3860, USA.,Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave., Suite 506, Boston, MA 02115, USA
| | - Andrew J Tatem
- WorldPop, Department of Geography and Environment, University of Southampton, University Road, Southampton, SO17 1BJ, UK.,Flowminder Foundation, Roslagsgatan 17, SE-11355, Stockholm, Sweden
| | - Robert C Reiner
- Department of Global Health and Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA 98121, USA
| | - T Alex Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, 345 Galvin Hall, Notre Dame, IN 46556, USA
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Lourenço J, Tennant W, Faria NR, Walker A, Gupta S, Recker M. Challenges in dengue research: A computational perspective. Evol Appl 2018; 11:516-533. [PMID: 29636803 PMCID: PMC5891037 DOI: 10.1111/eva.12554] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Accepted: 09/08/2017] [Indexed: 01/12/2023] Open
Abstract
The dengue virus is now the most widespread arbovirus affecting human populations, causing significant economic and social impact in South America and South-East Asia. Increasing urbanization and globalization, coupled with insufficient resources for control, misguided policies or lack of political will, and expansion of its mosquito vectors are some of the reasons why interventions have so far failed to curb this major public health problem. Computational approaches have elucidated on dengue's population dynamics with the aim to provide not only a better understanding of the evolution and epidemiology of the virus but also robust intervention strategies. It is clear, however, that these have been insufficient to address key aspects of dengue's biology, many of which will play a crucial role for the success of future control programmes, including vaccination. Within a multiscale perspective on this biological system, with the aim of linking evolutionary, ecological and epidemiological thinking, as well as to expand on classic modelling assumptions, we here propose, discuss and exemplify a few major computational avenues-real-time computational analysis of genetic data, phylodynamic modelling frameworks, within-host model frameworks and GPU-accelerated computing. We argue that these emerging approaches should offer valuable research opportunities over the coming years, as previously applied and demonstrated in the context of other pathogens.
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Affiliation(s)
| | - Warren Tennant
- Centre for Mathematics and the EnvironmentUniversity of ExeterPenrynUK
| | | | | | | | - Mario Recker
- Centre for Mathematics and the EnvironmentUniversity of ExeterPenrynUK
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38
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Kraemer MUG, Bisanzio D, Reiner RC, Zakar R, Hawkins JB, Freifeld CC, Smith DL, Hay SI, Brownstein JS, Perkins TA. Inferences about spatiotemporal variation in dengue virus transmission are sensitive to assumptions about human mobility: a case study using geolocated tweets from Lahore, Pakistan. EPJ DATA SCIENCE 2018; 7:16. [PMID: 30854281 PMCID: PMC6404370 DOI: 10.1140/epjds/s13688-018-0144-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 05/31/2018] [Indexed: 05/14/2023]
Abstract
UNLABELLED Billions of users of mobile phones, social media platforms, and other technologies generate an increasingly large volume of data that has the potential to be leveraged towards solving public health challenges. These and other big data resources tend to be most successful in epidemiological applications when utilized within an appropriate conceptual framework. Here, we demonstrate the importance of assumptions about host mobility in a framework for dynamic modeling of infectious disease spread among districts within a large urban area. Our analysis focused on spatial and temporal variation in the transmission of dengue virus (DENV) during a series of large seasonal epidemics in Lahore, Pakistan during 2011-2014. Similar to many directly transmitted diseases, DENV transmission occurs primarily where people spend time during daytime hours, given that DENV is transmitted by a day-biting mosquito. We inferred spatiotemporal variation in DENV transmission under five different assumptions about mobility patterns among ten districts of Lahore: no movement among districts, movement following patterns of geo-located tweets, movement proportional to district population size, and movement following the commonly used gravity and radiation models. Overall, we found that inferences about spatiotemporal variation in DENV transmission were highly sensitive to this range of assumptions about intra-urban human mobility patterns, although the three assumptions that allowed for a modest degree of intra-urban mobility all performed similarly in key respects. Differing inferences about transmission patterns based on our analysis are significant from an epidemiological perspective, as they have different implications for where control efforts should be targeted and whether conditions for transmission became more or less favorable over time. ELECTRONIC SUPPLEMENTARY MATERIAL The online version of this article (10.1140/epjds/s13688-018-0144-x) contains supplementary material.
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Affiliation(s)
- Moritz U. G. Kraemer
- Department of Pediatrics, Harvard Medical School, Boston, USA
- Computational Epidemiology Lab, Boston Children’s Hospital, Boston, USA
- Department of Zoology, University of Oxford, Oxford, UK
| | - D. Bisanzio
- RTI International, Washington, USA
- Center for Tropical Diseases, Sacro Cuore-Don Calabria Hospital, Negrar, Italy
| | - R. C. Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA
| | - R. Zakar
- Department of Public Health, University of Punjab, Lahore, Pakistan
| | - J. B. Hawkins
- Department of Pediatrics, Harvard Medical School, Boston, USA
- Computational Epidemiology Lab, Boston Children’s Hospital, Boston, USA
| | - C. C. Freifeld
- Computational Epidemiology Lab, Boston Children’s Hospital, Boston, USA
- College of Computer and Information Science, Northeastern University, Boston, USA
| | - D. L. Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA
- Sanaria Institute for Global Health and Tropical Medicine, Rockville, USA
| | - S. I. Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA
| | - J. S. Brownstein
- Department of Pediatrics, Harvard Medical School, Boston, USA
- Computational Epidemiology Lab, Boston Children’s Hospital, Boston, USA
| | - T. Alex Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, USA
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Schwartz FW, Liu G, Aggarwal P, Schwartz CM. Naïve Simplicity: The Overlooked Piece of the Complexity-Simplicity Paradigm. GROUND WATER 2017; 55:703-711. [PMID: 28742952 DOI: 10.1111/gwat.12570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Revised: 06/25/2017] [Accepted: 06/29/2017] [Indexed: 06/07/2023]
Abstract
Concepts of simplicity and complexity in modeling have been explored in papers, editorials, and talks. The concept is not well understood because there are at least two flavors of simplicity. Modelers envision simplicity (i.e., elegant simplicity) as the sought-after goal in modeling, but naïve simplicity, which is the focus of this paper, is commonly unrecognized and dangerous. The problem is that naïve or simple ideas are often mistaken for settled science and come with the prospect of being more wrong than right. The concept of the so-called simplicity cycle, in relation to classical problems of carbon-14 age and salinity in closed-basin lakes, is used to illustrate these points. The emerging problems of water-mosquitoes-diseases show the value of mapping new problems to the simplicity cycle. Researchers can "know what they do not know" and avoid the dangers of naïve simplicity.
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Affiliation(s)
| | | | - Pradeep Aggarwal
- Isotope Hydrology Section, International Atomic Energy Agency, Vienna, A1400, Austria
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40
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Grubaugh ND, Ladner JT, Kraemer MUG, Dudas G, Tan AL, Gangavarapu K, Wiley MR, White S, Thézé J, Magnani DM, Prieto K, Reyes D, Bingham AM, Paul LM, Robles-Sikisaka R, Oliveira G, Pronty D, Barcellona CM, Metsky HC, Baniecki ML, Barnes KG, Chak B, Freije CA, Gladden-Young A, Gnirke A, Luo C, MacInnis B, Matranga CB, Park DJ, Qu J, Schaffner SF, Tomkins-Tinch C, West KL, Winnicki SM, Wohl S, Yozwiak NL, Quick J, Fauver JR, Khan K, Brent SE, Reiner RC, Lichtenberger PN, Ricciardi MJ, Bailey VK, Watkins DI, Cone MR, Kopp EW, Hogan KN, Cannons AC, Jean R, Monaghan AJ, Garry RF, Loman NJ, Faria NR, Porcelli MC, Vasquez C, Nagle ER, Cummings DAT, Stanek D, Rambaut A, Sanchez-Lockhart M, Sabeti PC, Gillis LD, Michael SF, Bedford T, Pybus OG, Isern S, Palacios G, Andersen KG. Genomic epidemiology reveals multiple introductions of Zika virus into the United States. Nature 2017; 546:401-405. [PMID: 28538723 PMCID: PMC5536180 DOI: 10.1038/nature22400] [Citation(s) in RCA: 237] [Impact Index Per Article: 33.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Accepted: 04/28/2017] [Indexed: 12/23/2022]
Abstract
Zika virus (ZIKV) is causing an unprecedented epidemic linked to severe congenital abnormalities. In July 2016, mosquito-borne ZIKV transmission was reported in the continental United States; since then, hundreds of locally acquired infections have been reported in Florida. To gain insights into the timing, source, and likely route(s) of ZIKV introduction, we tracked the virus from its first detection in Florida by sequencing ZIKV genomes from infected patients and Aedes aegypti mosquitoes. We show that at least 4 introductions, but potentially as many as 40, contributed to the outbreak in Florida and that local transmission is likely to have started in the spring of 2016-several months before its initial detection. By analysing surveillance and genetic data, we show that ZIKV moved among transmission zones in Miami. Our analyses show that most introductions were linked to the Caribbean, a finding corroborated by the high incidence rates and traffic volumes from the region into the Miami area. Our study provides an understanding of how ZIKV initiates transmission in new regions.
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Affiliation(s)
- Nathan D Grubaugh
- Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, California 92037, USA
| | - Jason T Ladner
- Center for Genome Sciences, US Army Medical Research Institute of Infectious Diseases, Fort Detrick, Maryland 21702, USA
| | - Moritz U G Kraemer
- Department of Zoology, University of Oxford, Oxford OX1 3PS, UK
- Boston Children's Hospital, Boston, Massachusetts 02115, USA
- Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Gytis Dudas
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
| | - Amanda L Tan
- Department of Biological Sciences, College of Arts and Sciences, Florida Gulf Coast University, Fort Myers, Florida 33965, USA
| | - Karthik Gangavarapu
- Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, California 92037, USA
| | - Michael R Wiley
- Center for Genome Sciences, US Army Medical Research Institute of Infectious Diseases, Fort Detrick, Maryland 21702, USA
- College of Public Health, University of Nebraska Medical Center, Omaha, Nebraska 68198, USA
| | - Stephen White
- Bureau of Public Health Laboratories, Division of Disease Control and Health Protection, Florida Department of Health, Miami, Florida 33125, USA
| | - Julien Thézé
- Department of Zoology, University of Oxford, Oxford OX1 3PS, UK
| | - Diogo M Magnani
- Department of Pathology, University of Miami Miller School of Medicine, Miami, Florida 33136, USA
| | - Karla Prieto
- Center for Genome Sciences, US Army Medical Research Institute of Infectious Diseases, Fort Detrick, Maryland 21702, USA
- College of Public Health, University of Nebraska Medical Center, Omaha, Nebraska 68198, USA
| | - Daniel Reyes
- Center for Genome Sciences, US Army Medical Research Institute of Infectious Diseases, Fort Detrick, Maryland 21702, USA
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska 68198, USA
| | - Andrea M Bingham
- Bureau of Epidemiology, Division of Disease Control and Health Protection, Florida Department of Health, Tallahassee, Florida 32399, USA
| | - Lauren M Paul
- Department of Biological Sciences, College of Arts and Sciences, Florida Gulf Coast University, Fort Myers, Florida 33965, USA
| | - Refugio Robles-Sikisaka
- Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, California 92037, USA
| | - Glenn Oliveira
- Scripps Translational Science Institute, La Jolla, California 92037, USA
| | - Darryl Pronty
- Bureau of Public Health Laboratories, Division of Disease Control and Health Protection, Florida Department of Health, Miami, Florida 33125, USA
| | - Carolyn M Barcellona
- Department of Biological Sciences, College of Arts and Sciences, Florida Gulf Coast University, Fort Myers, Florida 33965, USA
| | - Hayden C Metsky
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Mary Lynn Baniecki
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Kayla G Barnes
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Bridget Chak
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Catherine A Freije
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | | | - Andreas Gnirke
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Cynthia Luo
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Bronwyn MacInnis
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | | | - Daniel J Park
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - James Qu
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | | | | | - Kendra L West
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Sarah M Winnicki
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Shirlee Wohl
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Nathan L Yozwiak
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Joshua Quick
- Institute of Microbiology and Infection, University of Birmingham, Birmingham B15 2TT, UK
| | - Joseph R Fauver
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado 80523, USA
| | - Kamran Khan
- Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario M5B 1T8, Canada
- Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Shannon E Brent
- Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario M5B 1T8, Canada
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Paola N Lichtenberger
- Division of Infectious Diseases, University of Miami Miller School of Medicine, Miami, Florida 33136, USA
| | - Michael J Ricciardi
- Department of Pathology, University of Miami Miller School of Medicine, Miami, Florida 33136, USA
| | - Varian K Bailey
- Department of Pathology, University of Miami Miller School of Medicine, Miami, Florida 33136, USA
| | - David I Watkins
- Department of Pathology, University of Miami Miller School of Medicine, Miami, Florida 33136, USA
| | - Marshall R Cone
- Bureau of Public Health Laboratories, Division of Disease Control and Health Protection, Florida Department of Health, Tampa, Florida 33612, USA
| | - Edgar W Kopp
- Bureau of Public Health Laboratories, Division of Disease Control and Health Protection, Florida Department of Health, Tampa, Florida 33612, USA
| | - Kelly N Hogan
- Bureau of Public Health Laboratories, Division of Disease Control and Health Protection, Florida Department of Health, Tampa, Florida 33612, USA
| | - Andrew C Cannons
- Bureau of Public Health Laboratories, Division of Disease Control and Health Protection, Florida Department of Health, Tampa, Florida 33612, USA
| | - Reynald Jean
- Florida Department of Health in Miami-Dade County, Miami, Florida 33125, USA
| | - Andrew J Monaghan
- National Center for Atmospheric Research, Boulder, Colorado 80307, USA
| | - Robert F Garry
- Department of Microbiology and Immunology, Tulane University School of Medicine, New Orleans, Louisiana 70112, USA
| | - Nicholas J Loman
- Institute of Microbiology and Infection, University of Birmingham, Birmingham B15 2TT, UK
| | - Nuno R Faria
- Department of Zoology, University of Oxford, Oxford OX1 3PS, UK
| | | | | | - Elyse R Nagle
- Center for Genome Sciences, US Army Medical Research Institute of Infectious Diseases, Fort Detrick, Maryland 21702, USA
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska 68198, USA
| | - Derek A T Cummings
- Department of Biology and Emerging Pathogens Institute, University of Florida, Gainesville, Florida 32610, USA
| | - Danielle Stanek
- Bureau of Epidemiology, Division of Disease Control and Health Protection, Florida Department of Health, Tallahassee, Florida 32399, USA
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Mariano Sanchez-Lockhart
- Center for Genome Sciences, US Army Medical Research Institute of Infectious Diseases, Fort Detrick, Maryland 21702, USA
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska 68198, USA
| | - Pardis C Sabeti
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts 02115, USA
- Howard Hughes Medical Institute, Chevy Chase, Maryland 20815, USA
| | - Leah D Gillis
- Bureau of Public Health Laboratories, Division of Disease Control and Health Protection, Florida Department of Health, Miami, Florida 33125, USA
| | - Scott F Michael
- Department of Biological Sciences, College of Arts and Sciences, Florida Gulf Coast University, Fort Myers, Florida 33965, USA
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford OX1 3PS, UK
| | - Sharon Isern
- Department of Biological Sciences, College of Arts and Sciences, Florida Gulf Coast University, Fort Myers, Florida 33965, USA
| | - Gustavo Palacios
- Center for Genome Sciences, US Army Medical Research Institute of Infectious Diseases, Fort Detrick, Maryland 21702, USA
| | - Kristian G Andersen
- Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, California 92037, USA
- Scripps Translational Science Institute, La Jolla, California 92037, USA
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, USA
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Vanlerberghe V, Gómez-Dantés H, Vazquez-Prokopec G, Alexander N, Manrique-Saide P, Coelho G, Toledo ME, Ocampo CB, Van der Stuyft P. Changing paradigms in Aedes control: considering the spatial heterogeneity of dengue transmission. REVISTA PANAMERICANA DE SALUD PUBLICA = PAN AMERICAN JOURNAL OF PUBLIC HEALTH 2017; 41:e16. [PMID: 31391815 PMCID: PMC6660874 DOI: 10.26633/rpsp.2017.16] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Accepted: 05/18/2016] [Indexed: 12/13/2022]
Abstract
Current dengue vector control strategies, focusing on reactive implementation of insecticide-based interventions in response to clinically apparent disease manifestations, tend to be inefficient, short-lived, and unsustainable within the worldwide epidemiological scenario of virus epidemic recrudescence. As a result of a series of expert meetings and deliberations, a paradigm shift is occurring and a new strategy, using risk stratification at the city level in order to concentrate proactive, sustained efforts in areas at high risk for transmission, has emerged. In this article, the authors 1) outline this targeted, proactive intervention strategy, within the context of dengue epidemiology, the dynamics of its transmission, and current Aedes control strategies, and 2) provide support from published literature for the need to empirically test its impact on dengue transmission as well as on the size of disease outbreaks. As chikungunya and Zika viruses continue to expand their range, the need for a science-based, proactive approach for control of urban Aedes spp. mosquitoes will become a central focus of integrated disease management planning.
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Affiliation(s)
- Veerle Vanlerberghe
- General Epidemiology and Disease Control Unit Institute of Tropical Medicine Antwerp Belgium General Epidemiology and Disease Control Unit, Institute of Tropical Medicine, Antwerp, Belgium
| | - Hector Gómez-Dantés
- Instituto Nacional de Salud Publica CuernavacaMorelos Mexico Instituto Nacional de Salud Publica, Cuernavaca, Morelos, Mexico
| | - Gonzalo Vazquez-Prokopec
- Department of Environmental Sciences Emory University AtlantaGeorgia United States of America Department of Environmental Sciences, Emory University, Atlanta, Georgia, United States of America
| | - Neal Alexander
- London School of Hygiene and Tropical Medicine London United Kingdom London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Pablo Manrique-Saide
- Entomological Bioassays Unit Universidad Autónoma de Yucatán, Merida Yucatán Mexico Entomological Bioassays Unit, Universidad Autónoma de Yucatán, Merida, Yucatán, Mexico
| | - Giovanini Coelho
- National Dengue Control Program Brazilian Ministry of Health Brasília Brazil National Dengue Control Program, Brazilian Ministry of Health, Brasília, Brazil
| | - Maria Eugenia Toledo
- Department of Epidemiology Institute of Tropical Medicine "Pedro Kourí," Havana Cuba Department of Epidemiology, Institute of Tropical Medicine "Pedro Kourí," Havana, Cuba
| | - Clara B Ocampo
- International Training and Medical Research Center Cali Colombia International Training and Medical Research Center, Cali, Colombia
| | - Patrick Van der Stuyft
- Department of Public Health Ghent University Ghent Belgium Department of Public Health, Ghent University, Ghent, Belgium
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42
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Manore CA, Ostfeld RS, Agusto FB, Gaff H, LaDeau SL. Defining the Risk of Zika and Chikungunya Virus Transmission in Human Population Centers of the Eastern United States. PLoS Negl Trop Dis 2017; 11:e0005255. [PMID: 28095405 PMCID: PMC5319773 DOI: 10.1371/journal.pntd.0005255] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Revised: 02/21/2017] [Accepted: 12/13/2016] [Indexed: 02/08/2023] Open
Abstract
The recent spread of mosquito-transmitted viruses and associated disease to the Americas motivates a new, data-driven evaluation of risk in temperate population centers. Temperate regions are generally expected to pose low risk for significant mosquito-borne disease; however, the spread of the Asian tiger mosquito (Aedes albopictus) across densely populated urban areas has established a new landscape of risk. We use a model informed by field data to assess the conditions likely to facilitate local transmission of chikungunya and Zika viruses from an infected traveler to Ae. albopictus and then to other humans in USA cities with variable human densities and seasonality. Mosquito-borne disease occurs when specific combinations of conditions maximize virus-to-mosquito and mosquito-to-human contact rates. We develop a mathematical model that captures the epidemiology and is informed by current data on vector ecology from urban sites. The model demonstrates that under specific but realistic conditions, fifty-percent of introductions by infectious travelers to a high human, high mosquito density city could initiate local transmission and 10% of the introductions could result in 100 or more people infected. Despite the propensity for Ae. albopictus to bite non-human vertebrates, we also demonstrate that local virus transmission and human outbreaks may occur when vectors feed from humans even just 40% of the time. Inclusion of human behavioral changes and mitigations were not incorporated into the models and would likely reduce predicted infections. This work demonstrates how a conditional series of non-average events can result in local arbovirus transmission and outbreaks of human disease, even in temperate cities.
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Affiliation(s)
- Carrie A. Manore
- Center for Computational Science Tulane University New Orleans, LA, United States of America
- Theoretical Biology and Biophysics Los Alamos National Laboratory Los Alamos, NM, United States of America
- New Mexico Consortium, Suite 301 Los Alamos, NM, United States of America
| | - Richard S. Ostfeld
- Cary Institute of Ecosystem Studies Box AB, 2801 Sharon Turnpike Millbrook, NY United States of America
| | - Folashade B. Agusto
- Department of Ecology and Evolutionary Biology University of Kansas Haworth Hall Lawrence, Kansas, United States of America
| | - Holly Gaff
- Department of Biological Sciences Old Dominion University Norfolk, VA, United States of America
- Mathematics, Statistics and Computer Science University of KwaZulu-Natal Durban, South Africa
| | - Shannon L. LaDeau
- Cary Institute of Ecosystem Studies Box AB, 2801 Sharon Turnpike Millbrook, NY United States of America
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43
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Kraemer MUG, Faria NR, Reiner RC, Golding N, Nikolay B, Stasse S, Johansson MA, Salje H, Faye O, Wint GRW, Niedrig M, Shearer FM, Hill SC, Thompson RN, Bisanzio D, Taveira N, Nax HH, Pradelski BSR, Nsoesie EO, Murphy NR, Bogoch II, Khan K, Brownstein JS, Tatem AJ, de Oliveira T, Smith DL, Sall AA, Pybus OG, Hay SI, Cauchemez S. Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015-16: a modelling study. THE LANCET. INFECTIOUS DISEASES 2016; 17:330-338. [PMID: 28017559 PMCID: PMC5332542 DOI: 10.1016/s1473-3099(16)30513-8] [Citation(s) in RCA: 150] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Revised: 10/28/2016] [Accepted: 11/09/2016] [Indexed: 11/25/2022]
Abstract
BACKGROUND Since late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock. METHODS We jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region. FINDINGS The early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5-7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34-0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52-0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13-0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92-0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected. INTERPRETATION Our findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy. FUNDING Wellcome Trust.
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Affiliation(s)
| | - Nuno R Faria
- Department of Zoology, University of Oxford, Oxford, UK
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Nick Golding
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK; School of BioSciences, University of Melbourne, Parkville, VIC, Australia
| | - Birgit Nikolay
- Mathematical Modelling of Infectious Diseases and Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France; Centre National de la Recherche Scientifique, URA 3012, Paris, France
| | - Stephanie Stasse
- Health Programme, European Commission, International Cooperation and Development, Delegation en RDC, Kinshasa, Democratic Republic of the Congo
| | - Michael A Johansson
- Centers for Disease Control and Prevention, San Juan, PR, USA; Center for Communicable Disease Dynamics, Harvard T H Chan School of Public Health, MA, USA
| | - Henrik Salje
- Mathematical Modelling of Infectious Diseases and Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France; Centre National de la Recherche Scientifique, URA 3012, Paris, France; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Ousmane Faye
- Arbovirus and Viral Hemorrhagic Fever Unit, Institut Pasteur da Dakar, Dakar, Senegal
| | - G R William Wint
- Environmental Research Group Oxford, Department of Zoology, Oxford, UK
| | | | - Freya M Shearer
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK
| | - Sarah C Hill
- Department of Zoology, University of Oxford, Oxford, UK
| | | | | | - Nuno Taveira
- Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, University of Lisbon, Portugal; Centro de Investigacao Interdisciplinar Egas Moniz, Instituto Superior de Ciencias da Saude Egas Moniz, Caparica, Portugal
| | - Heinrich H Nax
- Computational Social Science, ETH Zurich, Zurich, Switzerland
| | | | - Elaine O Nsoesie
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Nicholas R Murphy
- School of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Isaac I Bogoch
- Divisions of General Internal Medicine and Infectious Diseases, Toronto General Hospital, University Health Network, Toronto, ON, Canada
| | - Kamran Khan
- Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, ON, Canada
| | | | - Andrew J Tatem
- WorldPop, Department of Geography and Environment, University of Southampton, Southampton, UK; Flowminder Foundation, Stockholm, Sweden
| | - Tulio de Oliveira
- School of Laboratory Medicine and Medical Sciences, Nelson R Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - David L Smith
- Department of Zoology, University of Oxford, Oxford, UK; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Sanaria Institute for Global Health and Tropical Medicine, Rockville, MD, USA
| | - Amadou A Sall
- Arbovirus and Viral Hemorrhagic Fever Unit, Institut Pasteur da Dakar, Dakar, Senegal
| | | | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases and Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France; Centre National de la Recherche Scientifique, URA 3012, Paris, France
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Chowell G, Viboud C, Simonsen L, Moghadas SM. Characterizing the reproduction number of epidemics with early subexponential growth dynamics. J R Soc Interface 2016; 13:20160659. [PMID: 27707909 PMCID: PMC5095223 DOI: 10.1098/rsif.2016.0659] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Accepted: 09/07/2016] [Indexed: 11/12/2022] Open
Abstract
Early estimates of the transmission potential of emerging and re-emerging infections are increasingly used to inform public health authorities on the level of risk posed by outbreaks. Existing methods to estimate the reproduction number generally assume exponential growth in case incidence in the first few disease generations, before susceptible depletion sets in. In reality, outbreaks can display subexponential (i.e. polynomial) growth in the first few disease generations, owing to clustering in contact patterns, spatial effects, inhomogeneous mixing, reactive behaviour changes or other mechanisms. Here, we introduce the generalized growth model to characterize the early growth profile of outbreaks and estimate the effective reproduction number, with no need for explicit assumptions about the shape of epidemic growth. We demonstrate this phenomenological approach using analytical results and simulations from mechanistic models, and provide validation against a range of empirical disease datasets. Our results suggest that subexponential growth in the early phase of an epidemic is the rule rather the exception. Mechanistic simulations show that slight modifications to the classical susceptible-infectious-removed model result in subexponential growth, and in turn a rapid decline in the reproduction number within three to five disease generations. For empirical outbreaks, the generalized-growth model consistently outperforms the exponential model for a variety of directly and indirectly transmitted diseases datasets (pandemic influenza, measles, smallpox, bubonic plague, cholera, foot-and-mouth disease, HIV/AIDS and Ebola) with model estimates supporting subexponential growth dynamics. The rapid decline in effective reproduction number predicted by analytical results and observed in real and synthetic datasets within three to five disease generations contrasts with the expectation of invariant reproduction number in epidemics obeying exponential growth. The generalized-growth concept also provides us a compelling argument for the unexpected extinction of certain emerging disease outbreaks during the early ascending phase. Overall, our approach promotes a more reliable and data-driven characterization of the early epidemic phase, which is important for accurate estimation of the reproduction number and prediction of disease impact.
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Affiliation(s)
- Gerardo Chowell
- School of Public Health, Georgia State University, Atlanta, GA, USA Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Cécile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Lone Simonsen
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark Department of Global Health, George Washington University, Washington, DC, USA
| | - Seyed M Moghadas
- Agent Based Modelling Laboratory, York University, Toronto, Canada
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45
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Cheng Q, Lu X, Wu JT, Liu Z, Huang J. Analysis of heterogeneous dengue transmission in Guangdong in 2014 with multivariate time series model. Sci Rep 2016; 6:33755. [PMID: 27666657 PMCID: PMC5036033 DOI: 10.1038/srep33755] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Accepted: 09/02/2016] [Indexed: 12/12/2022] Open
Abstract
Guangdong experienced the largest dengue epidemic in recent history. In 2014, the number of dengue cases was the highest in the previous 10 years and comprised more than 90% of all cases. In order to analyze heterogeneous transmission of dengue, a multivariate time series model decomposing dengue risk additively into endemic, autoregressive and spatiotemporal components was used to model dengue transmission. Moreover, random effects were introduced in the model to deal with heterogeneous dengue transmission and incidence levels and power law approach was embedded into the model to account for spatial interaction. There was little spatial variation in the autoregressive component. In contrast, for the endemic component, there was a pronounced heterogeneity between the Pearl River Delta area and the remaining districts. For the spatiotemporal component, there was considerable heterogeneity across districts with highest values in some western and eastern department. The results showed that the patterns driving dengue transmission were found by using clustering analysis. And endemic component contribution seems to be important in the Pearl River Delta area, where the incidence is high (95 per 100,000), while areas with relatively low incidence (4 per 100,000) are highly dependent on spatiotemporal spread and local autoregression.
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Affiliation(s)
- Qing Cheng
- Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, 410073 Changsha, China.,College of Information System and Management, National University of Defense Technology, 410073 Changsha, China
| | - Xin Lu
- College of Information System and Management, National University of Defense Technology, 410073 Changsha, China.,Flowminder Foundation, 17177 Stockholm, Sweden.,Department of Public Health Sciences, Karolinska Institutet, 17177 Stock-holm, Sweden.,Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Centre for Disease Control and Prevention, Beijing 102206, P. R. China
| | - Joseph T Wu
- School of Public Health, Li Kashing Faculty of Medicine, Hong Kong University, Hong Kong Special Administrative Region, China
| | - Zhong Liu
- Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, 410073 Changsha, China.,College of Information System and Management, National University of Defense Technology, 410073 Changsha, China
| | - Jincai Huang
- Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, 410073 Changsha, China.,College of Information System and Management, National University of Defense Technology, 410073 Changsha, China
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46
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Wijayanti SPM, Porphyre T, Chase-Topping M, Rainey SM, McFarlane M, Schnettler E, Biek R, Kohl A. The Importance of Socio-Economic Versus Environmental Risk Factors for Reported Dengue Cases in Java, Indonesia. PLoS Negl Trop Dis 2016; 10:e0004964. [PMID: 27603137 PMCID: PMC5014450 DOI: 10.1371/journal.pntd.0004964] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Accepted: 08/09/2016] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND Dengue is a major mosquito-borne viral disease and an important public health problem. Identifying which factors are important determinants in the risk of dengue infection is critical in supporting and guiding preventive measures. In South-East Asia, half of all reported fatal infections are recorded in Indonesia, yet little is known about the epidemiology of dengue in this country. METHODOLOGY/PRINCIPAL FINDINGS Hospital-reported dengue cases in Banyumas regency, Central Java were examined to build Bayesian spatial and spatio-temporal models assessing the influence of climatic, demographic and socio-economic factors on the risk of dengue infection. A socio-economic factor linking employment type and economic status was the most influential on the risk of dengue infection in the Regency. Other factors such as access to healthcare facilities and night-time temperature were also found to be associated with higher risk of reported dengue infection but had limited explanatory power. CONCLUSIONS/SIGNIFICANCE Our data suggest that dengue infections are triggered by indoor transmission events linked to socio-economic factors (employment type, economic status). Preventive measures in this area should therefore target also specific environments such as schools and work areas to attempt and reduce dengue burden in this community. Although our analysis did not account for factors such as variations in immunity which need further investigation, this study can advise preventive measures in areas with similar patterns of reported dengue cases and environment.
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Affiliation(s)
- Siwi P. M. Wijayanti
- MRC–University of Glasgow Centre for Virus Research, Glasgow, Scotland, United Kingdom
- Public Health Department, Faculty of Health Sciences, University of Jenderal Soedirman, Purwokerto, Indonesia
- * E-mail: (SPMW); (TP); (AK)
| | - Thibaud Porphyre
- Centre for Immunity, Infection and Evolution (CIIE), Ashworth Laboratories, University of Edinburgh, Edinburgh, United Kingdom
- * E-mail: (SPMW); (TP); (AK)
| | - Margo Chase-Topping
- Centre for Immunity, Infection and Evolution (CIIE), Ashworth Laboratories, University of Edinburgh, Edinburgh, United Kingdom
| | - Stephanie M. Rainey
- MRC–University of Glasgow Centre for Virus Research, Glasgow, Scotland, United Kingdom
| | - Melanie McFarlane
- MRC–University of Glasgow Centre for Virus Research, Glasgow, Scotland, United Kingdom
| | - Esther Schnettler
- MRC–University of Glasgow Centre for Virus Research, Glasgow, Scotland, United Kingdom
| | - Roman Biek
- MRC–University of Glasgow Centre for Virus Research, Glasgow, Scotland, United Kingdom
- Boyd Orr Centre for Population and Ecosystem Health, Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Alain Kohl
- MRC–University of Glasgow Centre for Virus Research, Glasgow, Scotland, United Kingdom
- * E-mail: (SPMW); (TP); (AK)
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47
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Chowell G, Sattenspiel L, Bansal S, Viboud C. Mathematical models to characterize early epidemic growth: A review. Phys Life Rev 2016; 18:66-97. [PMID: 27451336 PMCID: PMC5348083 DOI: 10.1016/j.plrev.2016.07.005] [Citation(s) in RCA: 178] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2016] [Revised: 07/01/2016] [Accepted: 07/02/2016] [Indexed: 10/21/2022]
Abstract
There is a long tradition of using mathematical models to generate insights into the transmission dynamics of infectious diseases and assess the potential impact of different intervention strategies. The increasing use of mathematical models for epidemic forecasting has highlighted the importance of designing reliable models that capture the baseline transmission characteristics of specific pathogens and social contexts. More refined models are needed however, in particular to account for variation in the early growth dynamics of real epidemics and to gain a better understanding of the mechanisms at play. Here, we review recent progress on modeling and characterizing early epidemic growth patterns from infectious disease outbreak data, and survey the types of mathematical formulations that are most useful for capturing a diversity of early epidemic growth profiles, ranging from sub-exponential to exponential growth dynamics. Specifically, we review mathematical models that incorporate spatial details or realistic population mixing structures, including meta-population models, individual-based network models, and simple SIR-type models that incorporate the effects of reactive behavior changes or inhomogeneous mixing. In this process, we also analyze simulation data stemming from detailed large-scale agent-based models previously designed and calibrated to study how realistic social networks and disease transmission characteristics shape early epidemic growth patterns, general transmission dynamics, and control of international disease emergencies such as the 2009 A/H1N1 influenza pandemic and the 2014-2015 Ebola epidemic in West Africa.
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Affiliation(s)
- Gerardo Chowell
- School of Public Health, Georgia State University, Atlanta, GA, USA; Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.
| | - Lisa Sattenspiel
- Department of Anthropology, University of Missouri, Columbia, MO, USA
| | - Shweta Bansal
- Department of Biology, Georgetown University, Washington DC, USA; Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Cécile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
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48
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Perkins TA, Siraj AS, Ruktanonchai CW, Kraemer MUG, Tatem AJ. Model-based projections of Zika virus infections in childbearing women in the Americas. Nat Microbiol 2016; 1:16126. [DOI: 10.1038/nmicrobiol.2016.126] [Citation(s) in RCA: 110] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 06/28/2016] [Indexed: 01/22/2023]
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49
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Messina JP, Kraemer MU, Brady OJ, Pigott DM, Shearer FM, Weiss DJ, Golding N, Ruktanonchai CW, Gething PW, Cohn E, Brownstein JS, Khan K, Tatem AJ, Jaenisch T, Murray CJ, Marinho F, Scott TW, Hay SI. Mapping global environmental suitability for Zika virus. eLife 2016; 5. [PMID: 27090089 PMCID: PMC4889326 DOI: 10.7554/elife.15272] [Citation(s) in RCA: 237] [Impact Index Per Article: 29.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2016] [Accepted: 04/10/2016] [Indexed: 01/07/2023] Open
Abstract
Zika virus was discovered in Uganda in 1947 and is transmitted by Aedes mosquitoes, which also act as vectors for dengue and chikungunya viruses throughout much of the tropical world. In 2007, an outbreak in the Federated States of Micronesia sparked public health concern. In 2013, the virus began to spread across other parts of Oceania and in 2015, a large outbreak in Latin America began in Brazil. Possible associations with microcephaly and Guillain-Barré syndrome observed in this outbreak have raised concerns about continued global spread of Zika virus, prompting its declaration as a Public Health Emergency of International Concern by the World Health Organization. We conducted species distribution modelling to map environmental suitability for Zika. We show a large portion of tropical and sub-tropical regions globally have suitable environmental conditions with over 2.17 billion people inhabiting these areas. DOI:http://dx.doi.org/10.7554/eLife.15272.001 Zika virus is transmitted between humans by mosquitoes. The majority of infections cause mild flu-like symptoms, but neurological complications in adults and infants have been found in recent outbreaks. Although it was discovered in Uganda in 1947, Zika only caused sporadic infections in humans until 2007, when it caused a large outbreak in the Federated States of Micronesia. The virus later spread across Oceania, was first reported in Brazil in 2015 and has since rapidly spread across Latin America. This has led many people to question how far it will continue to spread. There was therefore a need to define the areas where the virus could be transmitted, including the human populations that might be risk in these areas. Messina et al. have now mapped the areas that provide conditions that are highly suitable for the spread of the Zika virus. These areas occur in many tropical and sub-tropical regions around the globe. The largest areas of risk in the Americas lie in Brazil, Colombia and Venezuela. Although Zika has yet to be reported in the USA, a large portion of the southeast region from Texas through to Florida is highly suitable for transmission. Much of sub-Saharan Africa (where several sporadic cases have been reported since the 1950s) also presents an environment that is highly suitable for the Zika virus. While no cases have yet been reported in India, a large portion of the subcontinent is also suitable for Zika transmission. Over 2 billion people live in Zika-suitable areas globally, and in the Americas alone, over 5.4 million births occurred in 2015 within such areas. It is important, however, to recognize that not all individuals living in suitable areas will necessarily be exposed to Zika. We still lack a great deal of basic epidemiological information about Zika. More needs to be known about the species of mosquito that spreads the disease and how the Zika virus interacts with related viruses such as dengue. As such information becomes available and clinical cases become routinely diagnosed, the global evidence base will be strengthened, which will improve the accuracy of future maps. DOI:http://dx.doi.org/10.7554/eLife.15272.002
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Affiliation(s)
- Jane P Messina
- Department of Zoology, University of Oxford, Oxford, United Kingdom
| | | | - Oliver J Brady
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - David M Pigott
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.,Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States
| | - Freya M Shearer
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Daniel J Weiss
- Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Nick Golding
- Department of BioSciences, University of Melbourne, Parkville, United Kingdom
| | - Corrine W Ruktanonchai
- WorldPop project, Department of Geography and Environment, University of Southampton, Southampton, United Kingdom
| | - Peter W Gething
- Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Emily Cohn
- Boston Children's Hospital, Harvard Medical School, Boston, United Kingdom
| | - John S Brownstein
- Boston Children's Hospital, Harvard Medical School, Boston, United Kingdom
| | - Kamran Khan
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada.,Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Canada
| | - Andrew J Tatem
- WorldPop project, Department of Geography and Environment, University of Southampton, Southampton, United Kingdom.,Flowminder Foundation, Stockholm, Sweden
| | - Thomas Jaenisch
- Section Clinical Tropical Medicine, Department for Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany.,German Centre for Infection Research (DZIF), Heidelberg partner site, Heidelberg, Germany
| | - Christopher Jl Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States
| | - Fatima Marinho
- Secretariat of Health Surveillance, Ministry of Health Brazil, Brasilia, Brazil
| | - Thomas W Scott
- Department of Entomology and Nematology, University of California Davis, Davis, United States
| | - Simon I Hay
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.,Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States
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50
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Fatima SH, Atif S, Rasheed SB, Zaidi F, Hussain E. Species Distribution Modelling ofAedes aegyptiin two dengue-endemic regions of Pakistan. Trop Med Int Health 2016; 21:427-36. [DOI: 10.1111/tmi.12664] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Syeda Hira Fatima
- Department of Space Science; Institute of Space Technology; Islamabad Pakistan
| | - Salman Atif
- Institute of Geographical Information System; National University of Sciences and Technology; Karachi Pakistan
| | | | - Farrah Zaidi
- Department of Zoology; University of Peshawar; Peshawar Pakistan
| | - Ejaz Hussain
- Institute of Geographical Information System; National University of Sciences and Technology; Karachi Pakistan
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