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Janko MM, Araujo AL, Ascencio EJ, Guedes GR, Vasco LE, Santos RO, Damasceno CP, Medrano PG, Chacón-Uscamaita PR, Gunderson AK, O'Malley S, Kansara PH, Narvaez MB, Coombes C, Pizzitutti F, Salmon-Mulanovich G, Zaitchik BF, Mena CF, Lescano AG, Barbieri AF, Pan WK. Study protocol: improving response to malaria in the Amazon through identification of inter-community networks and human mobility in border regions of Ecuador, Peru and Brazil. BMJ Open 2024; 14:e078911. [PMID: 38626977 PMCID: PMC11029361 DOI: 10.1136/bmjopen-2023-078911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 02/29/2024] [Indexed: 04/19/2024] Open
Abstract
INTRODUCTION Understanding human mobility's role in malaria transmission is critical to successful control and elimination. However, common approaches to measuring mobility are ill-equipped for remote regions such as the Amazon. This study develops a network survey to quantify the effect of community connectivity and mobility on malaria transmission. METHODS We measure community connectivity across the study area using a respondent driven sampling design among key informants who are at least 18 years of age. 45 initial communities will be selected: 10 in Brazil, 10 in Ecuador and 25 in Peru. Participants will be recruited in each initial node and administered a survey to obtain data on each community's mobility patterns. Survey responses will be ranked and the 2-3 most connected communities will then be selected and surveyed. This process will be repeated for a third round of data collection. Community network matrices will be linked with each country's malaria surveillance system to test the effects of mobility on disease risk. ETHICS AND DISSEMINATION This study protocol has been approved by the institutional review boards of Duke University (USA), Universidad San Francisco de Quito (Ecuador), Universidad Peruana Cayetano Heredia (Peru) and Universidade Federal Minas Gerais (Brazil). Results will be disseminated in communities by the end of the study.
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Affiliation(s)
- Mark M Janko
- Duke Global Health Institute, Durham, North Carolina, USA
| | - Andrea L Araujo
- Instituto de Geografia, Universidad San Francisco de Quito, Quito, Ecuador
| | - Edson J Ascencio
- Emerge, Emerging Diseases and Climate Change Research Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Gilvan R Guedes
- Center for Regional Development and Planning (Cedeplar), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Luis E Vasco
- Instituto de Geografia, Universidad San Francisco de Quito, Quito, Ecuador
| | - Reinaldo O Santos
- Center for Regional Development and Planning (Cedeplar), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Camila P Damasceno
- Center for Regional Development and Planning (Cedeplar), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | | | - Pamela R Chacón-Uscamaita
- Emerge, Emerging Diseases and Climate Change Research Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Annika K Gunderson
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Sara O'Malley
- Duke University Nicholas School of the Environment, Durham, North Carolina, USA
| | - Prakrut H Kansara
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, USA
| | - Manuel B Narvaez
- Instituto de Geografia, Universidad San Francisco de Quito, Quito, Ecuador
| | - Carolina Coombes
- Emerge, Emerging Diseases and Climate Change Research Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru
| | | | | | - Benjamin F Zaitchik
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, USA
| | - Carlos F Mena
- Instituto de Geografia, Universidad San Francisco de Quito, Quito, Ecuador
| | - Andres G Lescano
- Emerge, Emerging Diseases and Climate Change Research Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Alisson F Barbieri
- Center for Regional Development and Planning (Cedeplar), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - William K Pan
- Duke Global Health Institute, Durham, North Carolina, USA
- Duke University Nicholas School of the Environment, Durham, North Carolina, USA
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Janko MM, Araujo AL, Ascencio EJ, Guedes GR, Vasco LE, Santos RA, Damasceno CP, Medrano PG, Chacón-Uscamaita PR, Gunderson AK, O’Malley S, Kansara PH, Narvaez MB, Coombes CS, Pizzitutti F, Salmon-Mulanovich G, Zaitchik BF, Mena CF, Lescano AG, Barbieri AF, Pan WK. Network Profile: Improving Response to Malaria in the Amazon through Identification of Inter-Community Networks and Human Mobility in Border Regions of Ecuador, Peru, and Brazil. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.29.23299202. [PMID: 38076857 PMCID: PMC10705622 DOI: 10.1101/2023.11.29.23299202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Objectives Understanding human mobility's role on malaria transmission is critical to successful control and elimination. However, common approaches to measuring mobility are ill-equipped for remote regions such as the Amazon. This study develops a network survey to quantify the effect of community connectivity and mobility on malaria transmission. Design A community-level network survey. Setting We collect data on community connectivity along three river systems in the Amazon basin: the Pastaza river corridor spanning the Ecuador-Peru border; and the Amazon and Javari river corridors spanning the Brazil-Peru border. Participants We interviewed key informants in Brazil, Ecuador, and Peru, including from indigenous communities: Shuar, Achuar, Shiwiar, Kichwa, Ticuna, and Yagua. Key informants are at least 18 years of age and are considered community leaders. Primary outcome Weekly, community-level malaria incidence during the study period. Methods We measure community connectivity across the study area using a respondent driven sampling design. Forty-five communities were initially selected: 10 in Brazil, 10 in Ecuador, and 25 in Peru. Participants were recruited in each initial node and administered a survey to obtain data on each community's mobility patterns. Survey responses were ranked and the 2-3 most connected communities were then selected and surveyed. This process was repeated for a third round of data collection. Community network matrices will be linked with eadch country's malaria surveillance system to test the effects of mobility on disease risk. Findings To date, 586 key informants were surveyed from 126 communities along the Pastaza river corridor. Data collection along the Amazon and Javari river corridors is ongoing. Initial results indicate that network sampling is a superior method to delineate migration flows between communities. Conclusions Our study provides measures of mobility and connectivity in rural settings where traditional approaches are insufficient, and will allow us to understand mobility's effect on malaria transmission.
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Affiliation(s)
- Mark M. Janko
- Duke Global Health Institute, Duke University, Durham, North Carolina, USA
| | - Andrea L. Araujo
- Instituto de Geografía, Universidad San Francisco de Quito, Quito, Ecuador
| | - Edson J. Ascencio
- Emerge, Emerging Diseases and Climate Change Research Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Gilvan R. Guedes
- Center for Regional Development and Planning (Cedeplar), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Luis E. Vasco
- Instituto de Geografía, Universidad San Francisco de Quito, Quito, Ecuador
| | - Reinaldo A. Santos
- Center for Regional Development and Planning (Cedeplar), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Camila P. Damasceno
- Center for Regional Development and Planning (Cedeplar), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Perla G. Medrano
- Duke Global Health Institute, Duke University, Durham, North Carolina, USA
| | - Pamela R. Chacón-Uscamaita
- Emerge, Emerging Diseases and Climate Change Research Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Annika K. Gunderson
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Sara O’Malley
- Duke Global Health Institute, Duke University, Durham, North Carolina, USA
| | - Prakrut H. Kansara
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, USA
| | - Manuel B. Narvaez
- Instituto de Geografía, Universidad San Francisco de Quito, Quito, Ecuador
| | - Carolina S. Coombes
- Emerge, Emerging Diseases and Climate Change Research Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru
| | | | | | - Benjamin F. Zaitchik
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, USA
| | - Carlos F. Mena
- Instituto de Geografía, Universidad San Francisco de Quito, Quito, Ecuador
| | - Andres G. Lescano
- Emerge, Emerging Diseases and Climate Change Research Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Alisson F. Barbieri
- Center for Regional Development and Planning (Cedeplar), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - William K. Pan
- Duke Global Health Institute, Duke University, Durham, North Carolina, USA
- Nicholas School of the Environment, Duke University, Durham, North Carolina, USA
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Blake A, Hazel A, Jakurama J, Matundu J, Bharti N. Disparities in mobile phone ownership reflect inequities in access to healthcare. PLOS DIGITAL HEALTH 2023; 2:e0000270. [PMID: 37410708 DOI: 10.1371/journal.pdig.0000270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 05/05/2023] [Indexed: 07/08/2023]
Abstract
Human movement and population connectivity inform infectious disease management. Remote data, particularly mobile phone usage data, are frequently used to track mobility in outbreak response efforts without measuring representation in target populations. Using a detailed interview instrument, we measure population representation in phone ownership, mobility, and access to healthcare in a highly mobile population with low access to health care in Namibia, a middle-income country. We find that 1) phone ownership is both low and biased by gender, 2) phone ownership is correlated with differences in mobility and access to healthcare, and 3) reception is spatially unequal and scarce in non-urban areas. We demonstrate that mobile phone data do not represent the populations and locations that most need public health improvements. Finally, we show that relying on these data to inform public health decisions can be harmful with the potential to magnify health inequities rather than reducing them. To reduce health inequities, it is critical to integrate multiple data streams with measured, non-overlapping biases to ensure data representativeness for vulnerable populations.
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Affiliation(s)
- Alexandre Blake
- Biology Department, Center for Infectious Disease Dynamics, Penn State University, University Park, Pennsylvania, United States of America
| | - Ashley Hazel
- Francis I. Proctor Foundation, University of California, San Francisco, California, United States of America
| | | | | | - Nita Bharti
- Biology Department, Center for Infectious Disease Dynamics, Penn State University, University Park, Pennsylvania, United States of America
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Wu SL, Henry JM, Citron DT, Mbabazi Ssebuliba D, Nakakawa Nsumba J, Sánchez C HM, Brady OJ, Guerra CA, García GA, Carter AR, Ferguson HM, Afolabi BE, Hay SI, Reiner RC, Kiware S, Smith DL. Spatial dynamics of malaria transmission. PLoS Comput Biol 2023; 19:e1010684. [PMID: 37307282 DOI: 10.1371/journal.pcbi.1010684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 05/15/2023] [Indexed: 06/14/2023] Open
Abstract
The Ross-Macdonald model has exerted enormous influence over the study of malaria transmission dynamics and control, but it lacked features to describe parasite dispersal, travel, and other important aspects of heterogeneous transmission. Here, we present a patch-based differential equation modeling framework that extends the Ross-Macdonald model with sufficient skill and complexity to support planning, monitoring and evaluation for Plasmodium falciparum malaria control. We designed a generic interface for building structured, spatial models of malaria transmission based on a new algorithm for mosquito blood feeding. We developed new algorithms to simulate adult mosquito demography, dispersal, and egg laying in response to resource availability. The core dynamical components describing mosquito ecology and malaria transmission were decomposed, redesigned and reassembled into a modular framework. Structural elements in the framework-human population strata, patches, and aquatic habitats-interact through a flexible design that facilitates construction of ensembles of models with scalable complexity to support robust analytics for malaria policy and adaptive malaria control. We propose updated definitions for the human biting rate and entomological inoculation rates. We present new formulas to describe parasite dispersal and spatial dynamics under steady state conditions, including the human biting rates, parasite dispersal, the "vectorial capacity matrix," a human transmitting capacity distribution matrix, and threshold conditions. An [Formula: see text] package that implements the framework, solves the differential equations, and computes spatial metrics for models developed in this framework has been developed. Development of the model and metrics have focused on malaria, but since the framework is modular, the same ideas and software can be applied to other mosquito-borne pathogen systems.
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Affiliation(s)
- Sean L Wu
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
| | - John M Henry
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
- Quantitative Ecology and Resource Management, University of Washington, Seattle, Washington, United States of America
| | - Daniel T Citron
- Department of Population Health, Grossman School of Medicine, New York University, New York, New York, United States of America
| | | | - Juliet Nakakawa Nsumba
- Department of Mathematics, Makerere University Department of Mathematics, School of Physical Sciences, College of Natural Science, Makerere University, Kampala, Uganda
| | - Héctor M Sánchez C
- Division of Epidemiology, School of Public Health, University of California Berkeley, Berkeley, California, United States of America
- Division of Biostatistics, School of Public Health, University of California Berkeley, Berkeley, California, United States of America
| | - Oliver J Brady
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Carlos A Guerra
- MCD Global Health, Silver Spring, Maryland, United States of America
| | | | - Austin R Carter
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
| | - Heather M Ferguson
- Faculty of Biomedical and Life Sciences, University of Glasgow, Glasgow, Scotland, United Kingdom
| | - Bakare Emmanuel Afolabi
- International Centre for Applied Mathematical Modelling and Data Analytics, Federal University Oye Ekiti, Ekiti State, Nigeria
- Department of Mathematics, Federal University Oye Ekiti, Ekiti State, Nigeria
| | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
- Department of Health Metrics Science, University of Washington, Seattle, Washington, United States of America
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
- Department of Health Metrics Science, University of Washington, Seattle, Washington, United States of America
| | - Samson Kiware
- Ifakara Health Institute, Dar es Salaam, Tanzania
- Pan-African Mosquito Control Association (PAMCA), Nairobi, Kenya
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
- Department of Health Metrics Science, University of Washington, Seattle, Washington, United States of America
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Das AM, Hetzel MW, Yukich JO, Stuck L, Fakih BS, Al-Mafazy AWH, Ali A, Chitnis N. Modelling the impact of interventions on imported, introduced and indigenous malaria infections in Zanzibar, Tanzania. Nat Commun 2023; 14:2750. [PMID: 37173317 PMCID: PMC10182017 DOI: 10.1038/s41467-023-38379-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
Malaria cases can be classified as imported, introduced or indigenous cases. The World Health Organization's definition of malaria elimination requires an area to demonstrate that no new indigenous cases have occurred in the last three years. Here, we present a stochastic metapopulation model of malaria transmission that distinguishes between imported, introduced and indigenous cases, and can be used to test the impact of new interventions in a setting with low transmission and ongoing case importation. We use human movement and malaria prevalence data from Zanzibar, Tanzania, to parameterise the model. We test increasing the coverage of interventions such as reactive case detection; implementing new interventions including reactive drug administration and treatment of infected travellers; and consider the potential impact of a reduction in transmission on Zanzibar and mainland Tanzania. We find that the majority of new cases on both major islands of Zanzibar are indigenous cases, despite high case importation rates. Combinations of interventions that increase the number of infections treated through reactive case detection or reactive drug administration can lead to substantial decreases in malaria incidence, but for elimination within the next 40 years, transmission reduction in both Zanzibar and mainland Tanzania is necessary.
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Affiliation(s)
- Aatreyee M Das
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland.
- University of Basel, Basel, Switzerland.
| | - Manuel W Hetzel
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Joshua O Yukich
- Center for Applied Malaria Research and Evaluation, Department of Tropical Medicine, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Logan Stuck
- Center for Applied Malaria Research and Evaluation, Department of Tropical Medicine, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
- Amsterdam Institute for Global Health and Development Amsterdam, Amsterdam, Netherlands
- Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Bakar S Fakih
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
- Ifakara Health Institute, Dar es Salaam, United Republic of Tanzania
| | - Abdul-Wahid H Al-Mafazy
- Zanzibar Malaria Elimination Programme, Zanzibar, United Republic of Tanzania
- Office of the Chief Government Statistician (OCGS), Zanzibar, United Republic of Tanzania
| | - Abdullah Ali
- Zanzibar Malaria Elimination Programme, Zanzibar, United Republic of Tanzania
| | - Nakul Chitnis
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland.
- University of Basel, Basel, Switzerland.
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Katale RN, Gemechu DB. Spatio-temporal analysis of malaria incidence and its risk factors in North Namibia. Malar J 2023; 22:149. [PMID: 37149600 PMCID: PMC10163860 DOI: 10.1186/s12936-023-04577-4] [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: 07/11/2022] [Accepted: 04/25/2023] [Indexed: 05/08/2023] Open
Abstract
BACKGROUND Millions of dollars have been spent in fighting malaria in Namibia. However, malaria remains a major public health concern in Namibia, mostly in Kavango West and East, Ohangwena and Zambezi region. The primary goal of this study was to fit a spatio-temporal model that profiles spatial variation in malaria risk areas and investigate possible associations between disease risk and environmental factors at the constituency level in highly risk northern regions of Namibia. METHODS Malaria data, climatic data, and population data were merged and Global spatial autocorrelation statistics (Moran's I) was used to detect the spatial autocorrelation of malaria cases while malaria occurrence clusters were identified using local Moran statistics. A hierarchical Bayesian CAR model (Besag, York and Mollie's model "BYM") known to be the best model for modelling the spatial and temporal effects was then fitted to examine climatic factors that might explain spatial/temporal variation of malaria infection in Namibia. RESULTS Average rainfall received on an annual basis and maximum temperature were found to have a significant spatial and temporal variation on malaria infection. Every mm increase in annual rainfall in a specific constituency in each year increases annual mean malaria cases by 0.6%, same to average maximum temperature. The posterior means of the time main effect (year t) showed a visible slightly increase in global trend from 2018 to 2020. CONCLUSION The study discovered that the spatial temporal model with both random and fixed effects best fit the model, which demonstrated a strong spatial and temporal heterogeneity distribution of malaria cases (spatial pattern) with high risk in most of the Kavango West and East outskirt constituencies, posterior relative risk (RR: 1.57 to 1.78).
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Affiliation(s)
- Remember Ndahalashili Katale
- Department of Mathematics, Statistics, and Actuarial Science, Faculty of Health, Natural Resources and Applied Sciences, School of Natural and Applied Sciences, Namibia University of Science and Technology, Windhoek, Namibia
| | - Dibaba Bayisa Gemechu
- Department of Mathematics, Statistics, and Actuarial Science, Faculty of Health, Natural Resources and Applied Sciences, School of Natural and Applied Sciences, Namibia University of Science and Technology, Windhoek, Namibia.
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Wardle J, Bhatia S, Kraemer MUG, Nouvellet P, Cori A. Gaps in mobility data and implications for modelling epidemic spread: A scoping review and simulation study. Epidemics 2023; 42:100666. [PMID: 36689876 DOI: 10.1016/j.epidem.2023.100666] [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: 03/07/2022] [Revised: 11/18/2022] [Accepted: 01/06/2023] [Indexed: 01/13/2023] Open
Abstract
Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases. We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest. Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings.
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Affiliation(s)
- Jack Wardle
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK
| | | | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK; School of Life Sciences, University of Sussex, Brighton, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK.
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An Overview of Malaria Transmission Mechanisms, Control, and Modeling. Med Sci (Basel) 2022; 11:medsci11010003. [PMID: 36649040 PMCID: PMC9844307 DOI: 10.3390/medsci11010003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 12/11/2022] [Accepted: 12/19/2022] [Indexed: 12/28/2022] Open
Abstract
In sub-Saharan Africa, malaria is a leading cause of mortality and morbidity. As a result of the interplay between many factors, the control of this disease can be challenging. However, few studies have demonstrated malaria's complexity, control, and modeling although this perspective could lead to effective policy recommendations. This paper aims to be a didactic material providing the reader with an overview of malaria. More importantly, using a system approach lens, we intend to highlight the debated topics and the multifaceted thematic aspects of malaria transmission mechanisms, while showing the control approaches used as well as the model supporting the dynamics of malaria. As there is a large amount of information on each subject, we have attempted to provide a basic understanding of malaria that needs to be further developed. Nevertheless, this study illustrates the importance of using a multidisciplinary approach to designing next-generation malaria control policies.
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Das AM, Hetzel MW, Yukich JO, Stuck L, Fakih BS, Al-mafazy AWH, Ali A, Chitnis N. The impact of reactive case detection on malaria transmission in Zanzibar in the presence of human mobility. Epidemics 2022; 41:100639. [PMID: 36343496 PMCID: PMC9758615 DOI: 10.1016/j.epidem.2022.100639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 09/02/2022] [Accepted: 10/03/2022] [Indexed: 12/29/2022] Open
Abstract
Malaria persists at low levels on Zanzibar despite the use of vector control and case management. We use a metapopulation model to investigate the role of human mobility in malaria persistence on Zanzibar, and the impact of reactive case detection. The model was parameterized using survey data on malaria prevalence, reactive case detection, and travel history. We find that in the absence of imported cases from mainland Tanzania, malaria would likely cease to persist on Zanzibar. We also investigate potential intervention scenarios that may lead to elimination, especially through changes to reactive case detection. While we find that some additional cases are removed by reactive case detection, a large proportion of cases are missed due to many infections having a low parasite density that go undetected by rapid diagnostic tests, a low rate of those infected with malaria seeking treatment, and a low rate of follow up at the household level of malaria cases detected at health facilities. While improvements in reactive case detection would lead to a reduction in malaria prevalence, none of the intervention scenarios tested here were sufficient to reach elimination. Imported cases need to be treated to have a substantial impact on prevalence.
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Affiliation(s)
- Aatreyee M. Das
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland,University of Basel, Basel, Switzerland,Corresponding author at: Swiss Tropical and Public Health Institute, Allschwil, Switzerland.
| | - Manuel W. Hetzel
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland,University of Basel, Basel, Switzerland
| | - Joshua O. Yukich
- Center for Applied Malaria Research and Evaluation, Department of Tropical Medicine, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Logan Stuck
- Center for Applied Malaria Research and Evaluation, Department of Tropical Medicine, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Bakar S. Fakih
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland,University of Basel, Basel, Switzerland,Ifakara Health Institute, Dar es Salaam, United Republic of Tanzania
| | | | - Abdullah Ali
- Zanzibar Malaria Elimination Programme, Zanzibar, United Republic of Tanzania
| | - Nakul Chitnis
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland,University of Basel, Basel, Switzerland
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Lubinda J, Bi Y, Haque U, Lubinda M, Hamainza B, Moore AJ. Spatio-temporal monitoring of health facility-level malaria trends in Zambia and adaptive scaling for operational intervention. COMMUNICATIONS MEDICINE 2022; 2:79. [PMID: 35789566 PMCID: PMC9249860 DOI: 10.1038/s43856-022-00144-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 06/15/2022] [Indexed: 12/02/2022] Open
Abstract
Background The spatial and temporal variability inherent in malaria transmission within countries implies that targeted interventions for malaria control in high-burden settings and subnational elimination are a practical necessity. Identifying the spatio-temporal incidence, risk, and trends at different administrative geographies within malaria-endemic countries and monitoring them in near real-time as change occurs is crucial for developing and introducing cost-effective, subnational control and elimination intervention strategies. Methods This study developed intelligent data analytics incorporating Bayesian trend and spatio-temporal Integrated Laplace Approximation models to analyse high-burden over 32 million reported malaria cases from 1743 health facilities in Zambia between 2009 and 2015. Results The results show that at least 5.4 million people live in catchment areas with increasing trends of malaria, covering over 47% of all health facilities, while 5.7 million people live in areas with a declining trend (95% CI), covering 27% of health facilities. A two-scale spatio-temporal trend comparison identified significant differences between health facilities and higher-level districts, and the pattern observed in the southeastern region of Zambia provides the first evidence of the impact of recently implemented localised interventions. Conclusions The results support our recommendation for an adaptive scaling approach when implementing national malaria monitoring, control and elimination strategies and a particular need for stratified subnational approaches targeting high-burden regions with increasing disease trends. Strong clusters along borders with highly endemic countries in the north and south of Zambia underscore the need for coordinated cross-border malaria initiatives and strategies. Malaria is an infectious disease that is widespread in many African countries. Malaria transmission within a country can vary between regions, so tailored interventions for malaria control and elimination targeted to different regions are necessary. To achieve this, it is important to measure and monitor the frequency of malaria infections, its risk, and trends at different geographic administrative scales. This study analysed over 32 million reported malaria cases from 1743 health facilities in Zambia between 2009 and 2015. The results showed an increasing national trend in malaria risk and malaria infection frequency and identified differences between health facility and district trends. These findings support a flexible approach when implementing and expanding national malaria monitoring, control and elimination strategies, especially in areas bordering countries where malaria is widespread, cross-border movement is common, and cross-border initiatives could be beneficial. Lubinda et al. analyse over 32 million health-facility reported malaria cases in Zambia (2009–15) to examine spatially-structured temporal trends. They observe overall increasing trends in risk and rates and highlight the potential benefits of using an adaptive scaling approach in national malaria strategies, and a need for cross-border initiatives.
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Valdano E, Okano JT, Colizza V, Mitonga HK, Blower S. Use of mobile phone data in HIV epidemic control. Lancet HIV 2022; 9:e820-e821. [PMID: 36460021 PMCID: PMC9762893 DOI: 10.1016/s2352-3018(22)00332-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 09/27/2022] [Accepted: 11/01/2022] [Indexed: 06/17/2023]
Affiliation(s)
- Eugenio Valdano
- Center for Biomedical Modeling, Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA; Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, IPLESP, F75012, Paris, France
| | - Justin T Okano
- Center for Biomedical Modeling, Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Vittoria Colizza
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, IPLESP, F75012, Paris, France
| | - Honore K Mitonga
- Department of Epidemiology and Biostatistics, School of Public Health, University of Namibia, Private Bag 13301, Windhoek, Namibia
| | - Sally Blower
- Center for Biomedical Modeling, Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA.
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Anupriya, Bansal P, Graham DJ. Modelling the propagation of infectious disease via transportation networks. Sci Rep 2022; 12:20572. [PMID: 36446795 PMCID: PMC9707165 DOI: 10.1038/s41598-022-24866-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 11/22/2022] [Indexed: 11/30/2022] Open
Abstract
The dynamics of human mobility have been known to play a critical role in the spread of infectious diseases like COVID-19. In this paper, we present a simple compact way to model the transmission of infectious disease through transportation networks using widely available aggregate mobility data in the form of a zone-level origin-destination (OD) travel flow matrix. A key feature of our model is that it not only captures the propagation of infection via direct connections between zones (first-order effects) as in most existing studies but also transmission effects that are due to subsequent interactions in the remainder of the system (higher-order effects). We demonstrate the importance of capturing higher-order effects in a simulation study. We then apply our model to study the first wave of COVID-19 infections in (i) Italy, and, (ii) the New York Tri-State area. We use daily data on mobility between Italian provinces (province-level OD data) and between Tri-State Area counties (county-level OD data), and daily reported caseloads at the same geographical levels. Our empirical results indicate substantial predictive power, particularly during the early stages of the outbreak. Our model forecasts at least 85% of the spatial variation in observed weekly COVID-19 cases. Most importantly, our model delivers crucial metrics to identify target areas for intervention.
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Affiliation(s)
- Anupriya
- grid.7445.20000 0001 2113 8111Transport Strategy Centre, Department of Civil and Environmental Engineering, Imperial College London, London, SW7 2AZ UK
| | - Prateek Bansal
- grid.4280.e0000 0001 2180 6431Department of Civil and Environmental Engineering, National University of Singapore, Queenstown, 119077 Singapore
| | - Daniel J. Graham
- grid.7445.20000 0001 2113 8111Transport Strategy Centre, Department of Civil and Environmental Engineering, Imperial College London, London, SW7 2AZ UK
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Carrasco-Escobar G, Rosado J, Nolasco O, White MT, Mueller I, Castro MC, Rodriguez-Ferruci H, Gamboa D, Llanos-Cuentas A, Vinetz JM, Benmarhnia T. Effect of out-of-village working activities on recent malaria exposure in the Peruvian Amazon using parametric g-formula. Sci Rep 2022; 12:19144. [PMID: 36351988 PMCID: PMC9645738 DOI: 10.1038/s41598-022-23528-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 11/01/2022] [Indexed: 11/11/2022] Open
Abstract
In the Amazon Region of Peru, occupational activities are important drivers of human mobility and may increase the individual risk of being infected while contributing to increasing malaria community-level transmission. Even though out-of-village working activities and other mobility patterns have been identified as determinants of malaria transmission, no studies have quantified the effect of out-of-village working activities on recent malaria exposure and proposed plausible intervention scenarios. Using two population-based cross-sectional studies in the Loreto Department in Peru, and the parametric g-formula method, we simulated various hypothetical scenarios intervening in out-of-village working activities to reflect their potential health benefits. This study estimated that the standardized mean outcome (malaria seroprevalence) in the unexposed population (no out-of-village workers) was 44.6% (95% CI: 41.7%-47.5%) and 66.7% (95% CI: 61.6%-71.8%) in the exposed population resulting in a risk difference of 22.1% (95% CI: 16.3%-27.9%). However, heterogeneous patterns in the effects of interest were observed between peri-urban and rural areas (Cochran's Q test = 15.5, p < 0.001). Heterogeneous patterns were also observed in scenarios of increased prevalence of out-of-village working activities and restriction scenarios by gender (male vs. female) and age (18 and under vs. 19 and older) that inform possible occupational interventions targetting population subgroups. The findings of this study support the hypothesis that targeting out-of-village workers will considerably benefit current malaria elimination strategies in the Amazon Region. Particularly, males and adult populations that carried out out-of-village working activities in rural areas contribute the most to the malaria seropositivity (recent exposure to the parasite) in the Peruvian Amazon.
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Affiliation(s)
- Gabriel Carrasco-Escobar
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA.
- Health Innovation Lab, Institute of Tropical Medicine "Alexander Von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru.
| | - Jason Rosado
- G5 Épidémiologie Et Analyse Des Maladies Infectieuses, Département de Santé Globale, Institut Pasteur, 75015, Paris, France
| | - Oscar Nolasco
- Instituto de Medicina Tropical Alexander Von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación Y Desarrollo, Facultad de Ciencias Y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Michael T White
- G5 Épidémiologie Et Analyse Des Maladies Infectieuses, Département de Santé Globale, Institut Pasteur, 75015, Paris, France
| | - Ivo Mueller
- Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia
- Population Health and Immunity Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
| | - Marcia C Castro
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Dionicia Gamboa
- Instituto de Medicina Tropical Alexander Von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación Y Desarrollo, Facultad de Ciencias Y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
- Departamento de Ciencias Celulares Y Moleculares, Facultad de Ciencias Y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Alejandro Llanos-Cuentas
- Instituto de Medicina Tropical Alexander Von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Joseph M Vinetz
- Instituto de Medicina Tropical Alexander Von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación Y Desarrollo, Facultad de Ciencias Y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
- Section of Infectious Diseases, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Tarik Benmarhnia
- Scripps Institution of Oceanography, University of California, San Diego, CA, 92037, USA
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Saucedo O, Tien JH. Host movement, transmission hot spots, and vector-borne disease dynamics on spatial networks. Infect Dis Model 2022; 7:742-760. [DOI: 10.1016/j.idm.2022.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 09/04/2022] [Accepted: 10/26/2022] [Indexed: 11/06/2022] Open
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15
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Vargas Bernal E, Saucedo O, Tien JH. Relating Eulerian and Lagrangian spatial models for vector-host disease dynamics through a fundamental matrix. J Math Biol 2022; 84:57. [PMID: 35676373 DOI: 10.1007/s00285-022-01761-z] [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: 05/09/2021] [Revised: 01/21/2022] [Accepted: 05/11/2022] [Indexed: 11/26/2022]
Abstract
We explore the relationship between Eulerian and Lagrangian approaches for modeling movement in vector-borne diseases for discrete space. In the Eulerian approach we account for the movement of hosts explicitly through movement rates captured by a graph Laplacian matrix L. In the Lagrangian approach we only account for the proportion of time that individuals spend in foreign patches through a mixing matrix P. We establish a relationship between an Eulerian model and a Lagrangian model for the hosts in terms of the matrices L and P. We say that the two modeling frameworks are consistent if for a given matrix P, the matrix L can be chosen so that the residence times of the matrix P and the matrix L match. We find a sufficient condition for consistency, and examine disease quantities such as the final outbreak size and basic reproduction number in both the consistent and inconsistent cases. In the special case of a two-patch model, we observe how similar values for the basic reproduction number and final outbreak size can occur even in the inconsistent case. However, there are scenarios where the final sizes in both approaches can significantly differ by means of the relationship we propose.
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Affiliation(s)
| | - Omar Saucedo
- Department of Mathematics, Virginia Tech., Blacksburg, VA, USA
| | - Joseph Hua Tien
- Department of Mathematics, The Ohio State University, Columbus, OH, USA
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16
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Rae JD, Nosten S, Kajeechiwa L, Wiladphaingern J, Parker DM, Landier J, Thu AM, Dah H, Be A, Cho WC, Paw K, Paw ES, Shee PB, Poe C, Nu C, Nyaw B, Simpson JA, Devine A, Maude RJ, Moo KL, Min MC, Thwin MM, Tun SW, Nosten FH. Surveillance to achieve malaria elimination in eastern Myanmar: a 7-year observational study. Malar J 2022; 21:175. [PMID: 35672747 PMCID: PMC9171744 DOI: 10.1186/s12936-022-04175-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 05/05/2022] [Indexed: 12/02/2022] Open
Abstract
Background The collection and utilization of surveillance data is essential in monitoring progress towards achieving malaria elimination, in the timely response to increases in malaria case numbers and in the assessment of programme functioning. This paper describes the surveillance activities used by the malaria elimination task force (METF) programme which operates in eastern Myanmar, and provides an analysis of data collected from weekly surveillance, case investigations, and monitoring and evaluation of programme performance. Methods This retrospective analysis was conducted using data collected from a network of 1250 malaria posts operational between 2014 and 2021. To investigate changes in data completeness, malaria post performance, malaria case numbers, and the demographic details of malaria cases, summary statistics were used to compare data collected over space and time. Results In the first 3 years of the METF programme, improvements in data transmission routes resulted in a 18.9% reduction in late reporting, allowing for near real-time analysis of data collected at the malaria posts. In 2020, travel restrictions were in place across Karen State in response to COVID-19, and from February 2021 the military coup in Myanmar resulted in widescale population displacement. However, over that period there has been no decline in malaria post attendance, and the majority of consultations continue to occur within 48 h of fever onset. Case investigations found that 43.8% of cases travelled away from their resident village in the 3 weeks prior to diagnosis and 36.3% reported never using a bed net whilst sleeping in their resident village, which increased to 72.2% when sleeping away from their resident village. Malaria post assessments performed in 82.3% of the METF malaria posts found malaria posts generally performed to a high standard. Conclusions Surveillance data collected by the METF programme demonstrate that despite significant changes in the context in which the programme operates, malaria posts have remained accessible and continue to provide early diagnosis and treatment contributing to an 89.3% decrease in Plasmodium falciparum incidence between 2014 and 2021. Supplementary Information The online version contains supplementary material available at 10.1186/s12936-022-04175-w.
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Steinegger B, Iacopini I, Teixeira AS, Bracci A, Casanova-Ferrer P, Antonioni A, Valdano E. Non-selective distribution of infectious disease prevention may outperform risk-based targeting. Nat Commun 2022; 13:3028. [PMID: 35641538 PMCID: PMC9156732 DOI: 10.1038/s41467-022-30639-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 05/06/2022] [Indexed: 12/22/2022] Open
Abstract
Epidemic control often requires optimal distribution of available vaccines and prophylactic tools, to protect from infection those susceptible. Well-established theory recommends prioritizing those at the highest risk of exposure. But the risk is hard to estimate, especially for diseases involving stigma and marginalization. We address this conundrum by proving that one should target those at high risk only if the infection-averting efficacy of prevention is above a critical value, which we derive analytically. We apply this to the distribution of pre-exposure prophylaxis (PrEP) of the Human Immunodeficiency Virus (HIV) among men-having-sex-with-men (MSM), a population particularly vulnerable to HIV. PrEP is effective in averting infections, but its global scale-up has been slow, showing the need to revisit distribution strategies, currently risk-based. Using data from MSM communities in 58 countries, we find that non-selective PrEP distribution often outperforms risk-based, showing that a logistically simpler strategy is also more effective. Our theory may help design more feasible and successful prevention. Pre-exposure prophylaxis (PrEP) is an effective HIV prevention measure but identifying those most at risk to target for treatment is challenging. Here, the authors demonstrate that non-selective PrEP distribution outperforms targeted strategies when use is not consistent, and/or prevalence of untreated HIV is high.
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Affiliation(s)
- Benjamin Steinegger
- Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain
| | - Iacopo Iacopini
- Department of Network and Data Science, Central European University, Vienna, Austria.,Aix Marseille Univ, Université de Toulon, CNRS, CPT, Marseille, France
| | - Andreia Sofia Teixeira
- LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal.,INESC-ID, Lisboa, Portugal
| | - Alberto Bracci
- Department of Mathematics, City, University of London, London, UK
| | - Pau Casanova-Ferrer
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Department of Mathematics, Carlos III University of Madrid, Leganés, Spain.,Department of Systems Biology, Centro Nacional de Biotecnología, CNB-CSIC, Madrid, Spain
| | - Alberto Antonioni
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Department of Mathematics, Carlos III University of Madrid, Leganés, Spain
| | - Eugenio Valdano
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France.
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18
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Henry JM, Carter A, Smith DL. Infection age as a predictor of epidemiological metrics for malaria. Malar J 2022; 21:117. [PMID: 35392918 PMCID: PMC8991475 DOI: 10.1186/s12936-022-04134-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 03/22/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Accurate estimation of the burden of Plasmodium falciparum is essential for strategic planning for control and elimination. Due in part to the extreme heterogeneity in malaria exposure, immunity, other causes of disease, direct measurements of fever and disease attributable to malaria can be difficult. This can make a comparison of epidemiological metrics both within and between populations hard to interpret. An essential part of untangling this is an understanding of the complex time-course of malaria infections. METHODS Historic data from malariatherapy infections, in which individuals were intentionally infected with malaria parasites, were reexamined in aggregate. In this analysis, the age of each infection was examined as a potential predictor describing aggregate patterns across all infections. A series of piecewise linear and generalized linear regressions were performed to highlight the infection age-dependent patterns in both parasitaemia and gametocytaemia, and from parasitaemia and gametocytaemia to fever and transmission probabilities, respectively. RESULTS The observed duration of untreated patent infection was 130 days. As infections progressed, the fraction of infections subpatent by microscopy was seen to increase steadily. The time-averaged malaria infections had three distinct phases in parasitaemia: a growth phase for the first 6 days of patency, a rapid decline from day 6 to day 18, and a slowly declining chronic phase for the remaining duration of the infection. During the growth phase, parasite densities increased sharply to a peak. Densities sharply decline for a short period of time after the peak. During the chronic phase, infections declined steadily as infections age. gametocytaemia was strongly correlated with lagged asexual parasitaemia. Fever rates and transmission efficiency were strongly correlated with parasitaemia and gametocytaemia. The comparison between raw data and prediction from the age of infection has good qualitative agreement across all quantities of interest for predicting averaged effects. CONCLUSION The age of infection was established as a potentially useful covariate for malaria epidemiology. Infection age can be estimated given a history of exposure, and accounting for exposure history may potentially provide a new way to estimate malaria-attributable fever rates, transmission efficiency, and patent fraction in immunologically naïve individuals such as children and people in low-transmission regions. These data were collected from American adults with neurosyphilis, so there are reasons to be cautious about extending the quantitative results reported here to general populations in malaria-endemic regions. Understanding how immune responses modify these statistical relationships given past exposure is key for being able to apply these results more broadly.
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Affiliation(s)
- John M Henry
- College of the Environment, University of Washington, 1492 NE Boat St., 98105, Seattle, USA. .,Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, 98195, Seattle, USA.
| | - Austin Carter
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, 98195, Seattle, USA
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, 98195, Seattle, USA
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19
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Assessing the impact of human mobility to predict regional excess death in Ecuador. Sci Rep 2022; 12:370. [PMID: 35013374 PMCID: PMC8748783 DOI: 10.1038/s41598-021-03926-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 12/07/2021] [Indexed: 01/07/2023] Open
Abstract
COVID-19 outbreaks have had high mortality in low- and middle-income countries such as Ecuador. Human mobility is an important factor influencing the spread of diseases possibly leading to a high burden of disease at the country level. Drastic control measures, such as complete lockdown, are effective epidemic controls, yet in practice one hopes that a partial shutdown would suffice. It is an open problem to determine how much mobility can be allowed while controlling an outbreak. In this paper, we use statistical models to relate human mobility to the excess death in Ecuador while controlling for demographic factors. The mobility index provided by GRANDATA, based on mobile phone users, represents the change of number of out-of-home events with respect to a benchmark date (March 2nd, 2020). The study confirms the global trend that more men are dying than expected compared to women, and that people under 30 show less deaths than expected, particularly individuals younger than 20 with a death rate reduction between 22 and 27%. The weekly median mobility time series shows a sharp decrease in human mobility immediately after a national lockdown was declared on March 17, 2020 and a progressive increase towards the pre-lockdown level within two months. Relating median mobility to excess deaths shows a lag in its effect: first, a decrease in mobility in the previous two to three weeks decreases excess death and, more novel, we found an increase of mobility variability four weeks prior increases the number of excess deaths.
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Champagne C, Gerhards M, Lana J, García Espinosa B, Bradley C, González O, Cohen JM, Le Menach A, White MT, Pothin E. Using observed incidence to calibrate the transmission level of a mathematical model for Plasmodium vivax dynamics including case management and importation. Math Biosci 2021; 343:108750. [PMID: 34883106 PMCID: PMC8786669 DOI: 10.1016/j.mbs.2021.108750] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 10/29/2021] [Accepted: 10/29/2021] [Indexed: 11/27/2022]
Abstract
In this work, we present a simple and flexible model for Plasmodium vivax dynamics which can be easily combined with routinely collected data on local and imported case counts to quantify transmission intensity and simulate control strategies. This model extends the model from White et al. (2016) by including case management interventions targeting liver-stage or blood-stage parasites, as well as imported infections. The endemic steady state of the model is used to derive a relationship between the observed incidence and the transmission rate in order to calculate reproduction numbers and simulate intervention scenarios. To illustrate its potential applications, the model is used to calculate local reproduction numbers in Panama and identify areas of sustained malaria transmission that should be targeted by control interventions.
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Affiliation(s)
- Clara Champagne
- Swiss Tropical and Public Health Institute, Socinstrasse 57, P.O. Box, Basel, CH-4002, Switzerland; University of Basel, Petersplatz 1, P.O. Box, Basel, CH-4001, Switzerland.
| | - Maximilian Gerhards
- Swiss Tropical and Public Health Institute, Socinstrasse 57, P.O. Box, Basel, CH-4002, Switzerland; University of Basel, Petersplatz 1, P.O. Box, Basel, CH-4001, Switzerland
| | - Justin Lana
- Clinton Health Access Initiative, 383 Dorchester Ave, Suite 400, Boston, 02127, MA, USA
| | | | - Christina Bradley
- Clinton Health Access Initiative, 383 Dorchester Ave, Suite 400, Boston, 02127, MA, USA
| | - Oscar González
- Ministerio de Salud de Panama, Calle culebra, Edificio 265 del Ministerio de Salud, Corregimiento de Ancón, Panama
| | - Justin M Cohen
- Clinton Health Access Initiative, 383 Dorchester Ave, Suite 400, Boston, 02127, MA, USA
| | - Arnaud Le Menach
- Clinton Health Access Initiative, 383 Dorchester Ave, Suite 400, Boston, 02127, MA, USA
| | - Michael T White
- Institut Pasteur, Université de Paris, G5 Épidémiologie et Analyse des Maladies Infectieuses, Département de Santé Globale, Paris, F-75015, France
| | - Emilie Pothin
- Swiss Tropical and Public Health Institute, Socinstrasse 57, P.O. Box, Basel, CH-4002, Switzerland; University of Basel, Petersplatz 1, P.O. Box, Basel, CH-4001, Switzerland; Clinton Health Access Initiative, 383 Dorchester Ave, Suite 400, Boston, 02127, MA, USA
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Chang HH, Chang MC, Kiang M, Mahmud AS, Ekapirat N, Engø-Monsen K, Sudathip P, Buckee CO, Maude RJ. Low parasite connectivity among three malaria hotspots in Thailand. Sci Rep 2021; 11:23348. [PMID: 34857842 PMCID: PMC8640040 DOI: 10.1038/s41598-021-02746-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 11/17/2021] [Indexed: 12/25/2022] Open
Abstract
Identifying sources and sinks of malaria transmission is critical for designing effective intervention strategies particularly as countries approach elimination. The number of malaria cases in Thailand decreased 90% between 2012 and 2020, yet elimination has remained a major public health challenge with persistent transmission foci and ongoing importation. There are three main hotspots of malaria transmission in Thailand: Ubon Ratchathani and Sisaket in the Northeast; Tak in the West; and Yala in the South. However, the degree to which these hotspots are connected via travel and importation has not been well characterized. Here, we develop a metapopulation model parameterized by mobile phone call detail record data to estimate parasite flow among these regions. We show that parasite connectivity among these regions was limited, and that each of these provinces independently drove the malaria transmission in nearby provinces. Overall, our results suggest that due to the low probability of domestic importation between the transmission hotspots, control and elimination strategies can be considered separately for each region.
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Affiliation(s)
- Hsiao-Han Chang
- grid.38348.340000 0004 0532 0580Institute of Bioinformatics and Structural Biology and Department of Life Science, National Tsing Hua University, Hsinchu, Taiwan
| | - Meng-Chun Chang
- grid.38348.340000 0004 0532 0580Institute of Bioinformatics and Structural Biology and Department of Life Science, National Tsing Hua University, Hsinchu, Taiwan
| | - Mathew Kiang
- grid.168010.e0000000419368956Department of Epidemiology and Population Health, Stanford University, Stanford, CA USA
| | - Ayesha S. Mahmud
- grid.47840.3f0000 0001 2181 7878Department of Demography, University of California, Berkeley, USA
| | - Nattwut Ekapirat
- grid.10223.320000 0004 1937 0490Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | | | - Prayuth Sudathip
- grid.415836.d0000 0004 0576 2573Division of Vector Borne Diseases, Ministry of Public Health, Nonthaburi, Thailand
| | - Caroline O. Buckee
- grid.38142.3c000000041936754XHarvard TH Chan School of Public Health, Harvard University, Boston, USA
| | - Richard J. Maude
- grid.10223.320000 0004 1937 0490Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand ,grid.38142.3c000000041936754XHarvard TH Chan School of Public Health, Harvard University, Boston, USA ,grid.4991.50000 0004 1936 8948Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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22
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Sekandi JN, Murray K, Berryman C, Davis-Olwell P, Hurst C, Kakaire R, Kiwanuka N, Whalen CC, Mwaka ES. Ethical, Legal and Sociocultural Issues in the Use of Mobile Technologies and Call Detail Records Data for Public Health Research in the East African Region: A Scoping Review (Preprint). Interact J Med Res 2021; 11:e35062. [PMID: 35533323 PMCID: PMC9204580 DOI: 10.2196/35062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 02/17/2022] [Accepted: 04/14/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Juliet Nabbuye Sekandi
- Global Health Institute, College of Public Health, University of Georgia, Athens, GA, United States
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, United States
| | - Kenya Murray
- Global Health Institute, College of Public Health, University of Georgia, Athens, GA, United States
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, United States
| | - Corinne Berryman
- Department of Health Promotion and Behavior, College of Public Health, University of Georgia, Athens, GA, United States
| | - Paula Davis-Olwell
- Global Health Institute, College of Public Health, University of Georgia, Athens, GA, United States
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, United States
| | - Caroline Hurst
- Department of Health Promotion and Behavior, College of Public Health, University of Georgia, Athens, GA, United States
| | - Robert Kakaire
- Global Health Institute, College of Public Health, University of Georgia, Athens, GA, United States
| | - Noah Kiwanuka
- Department of Epidemiology and Biostatistics, School of Public Health, Makerere University, Kampala, Uganda
| | - Christopher C Whalen
- Global Health Institute, College of Public Health, University of Georgia, Athens, GA, United States
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, United States
| | - Erisa Sabakaki Mwaka
- Department of Anatomy, School of Biomedical Sciences, College of Health Sciences, Makerere University, Kampala, Uganda
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23
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Potgieter A, Fabris-Rotelli IN, Kimmie Z, Dudeni-Tlhone N, Holloway JP, Janse van Rensburg C, Thiede RN, Debba P, Manjoo-Docrat R, Abdelatif N, Khuluse-Makhanya S. Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa. Front Big Data 2021; 4:718351. [PMID: 34746771 PMCID: PMC8570263 DOI: 10.3389/fdata.2021.718351] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 09/06/2021] [Indexed: 11/17/2022] Open
Abstract
The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data sources convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices at a variety of spatial resolutions and further compares the results through hierarchical clustering. We consider four methods for constructing spatial weight matrices representing mobility between spatial units, taking into account distance between spatial units as well as spatial covariates. This provides insight for the user into which data provides what type of information and in what situations a particular data source is most useful.
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Affiliation(s)
- A Potgieter
- Department of Statistics, University of Pretoria, Pretoria, South Africa
| | - I N Fabris-Rotelli
- Department of Statistics, University of Pretoria, Pretoria, South Africa
| | - Z Kimmie
- Foundation of Human Rights, Johannesburg, South Africa
| | - N Dudeni-Tlhone
- Operational Intelligence, NextGen Enterprises and Institutions, Council for Scientific and Industrial Research, Pretoria, South Africa
| | - J P Holloway
- Operational Intelligence, NextGen Enterprises and Institutions, Council for Scientific and Industrial Research, Pretoria, South Africa
| | - C Janse van Rensburg
- Biostatistics Research Unit, South African Medical Research Council, Cape Town, South Africa
| | - R N Thiede
- Department of Statistics, University of Pretoria, Pretoria, South Africa
| | - P Debba
- Inclusive Smart Settlements and Regions, Smart Places, Council for Scientific and Industrial Research, Pretoria, South Africa.,Department of Statistics and Actuarial Science, University of Witwatersrand, Johannesburg, South Africa
| | - R Manjoo-Docrat
- Department of Statistics and Actuarial Science, University of Witwatersrand, Johannesburg, South Africa
| | - N Abdelatif
- Biostatistics Research Unit, South African Medical Research Council, Cape Town, South Africa
| | - S Khuluse-Makhanya
- IBM Research, Johannesburg, South Africa.,College of Graduate Studies, University of South Africa, Johannesburg, South Africa
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24
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Determining travel fluxes in epidemic areas. PLoS Comput Biol 2021; 17:e1009473. [PMID: 34705832 PMCID: PMC8550429 DOI: 10.1371/journal.pcbi.1009473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Accepted: 09/23/2021] [Indexed: 01/08/2023] Open
Abstract
Infectious diseases attack humans from time to time and threaten the lives and survival of people all around the world. An important strategy to prevent the spatial spread of infectious diseases is to restrict population travel. With the reduction of the epidemic situation, when and where travel restrictions can be lifted, and how to organize orderly movement patterns become critical and fall within the scope of this study. We define a novel diffusion distance derived from the estimated mobility network, based on which we provide a general model to describe the spatiotemporal spread of infectious diseases with a random diffusion process and a deterministic drift process of the population. We consequently develop a multi-source data fusion method to determine the population flow in epidemic areas. In this method, we first select available subregions in epidemic areas, and then provide solutions to initiate new travel flux among these subregions. To verify our model and method, we analyze the multi-source data from mainland China and obtain a new travel flux triggering scheme in the selected 29 cities with the most active population movements in mainland China. The testable predictions in these selected cities show that reopening the borders in accordance with our proposed travel flux will not cause a second outbreak of COVID-19 in these cities. The finding provides a methodology of re-triggering travel flux during the weakening spread stage of the epidemic. Human infectious diseases spread from their origins to other places with population movements. In order to curb the spatial spread of infectious diseases, many countries and regions may introduce some travel restrictions when the epidemic is severe, and reopen the borders as the epidemic eases. This process involves some important issues such as the start and end time of travel restrictions, the geographical scope of the implementation of the exit strategy, and the allowable passenger flow on traffic lines. Here, we integrate multi-source data with a mathematical model, and consequently develop a new method to determine the travel flux in epidemic areas. As an application, we use this method to calculate when and where the travel restrictions targeting COVID-19 in China in early 2020 could be lifted, and how to optimize passenger flow along the traffic lines among the reopened cities. The testable predictions indicate that the population flow in accordance with our proposed movement pattern will not cause a resurgent outbreak of COVID-19 in the cities studied.
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25
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Meredith HR, Giles JR, Perez-Saez J, Mande T, Rinaldo A, Mutembo S, Kabalo EN, Makungo K, Buckee CO, Tatem AJ, Metcalf CJE, Wesolowski A. Characterizing human mobility patterns in rural settings of sub-Saharan Africa. eLife 2021; 10:e68441. [PMID: 34533456 PMCID: PMC8448534 DOI: 10.7554/elife.68441] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 08/21/2021] [Indexed: 11/27/2022] Open
Abstract
Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.
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Affiliation(s)
- Hannah R Meredith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - John R Giles
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - Théophile Mande
- Bureau d'Etudes Scientifiques et Techniques - Eau, Energie, Environnement (BEST-3E), Ouagadougou, Burkina Faso
| | - Andrea Rinaldo
- Dipartimento di Ingegneria Civile Edile ed Ambientale, Università di Padova, Padova, Italy
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Simon Mutembo
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
- Macha Research Trust, Choma, Zambia
| | - Elliot N Kabalo
- Zambia Information and Communications Technology Authority, Lusaka, Zambia
| | | | - Caroline O Buckee
- Department of Epidemiology and the Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health, Boston, United States
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology and the Princeton School of Public and International Affairs, Princeton University, Princeton, United States
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
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26
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Rathinam F, Khatua S, Siddiqui Z, Malik M, Duggal P, Watson S, Vollenweider X. Using big data for evaluating development outcomes: A systematic map. CAMPBELL SYSTEMATIC REVIEWS 2021; 17:e1149. [PMID: 37051451 PMCID: PMC8354555 DOI: 10.1002/cl2.1149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
BACKGROUND Policy makers need access to reliable data to monitor and evaluate the progress of development outcomes and targets such as sustainable development outcomes (SDGs). However, significant data and evidence gaps remain. Lack of resources, limited capacity within governments and logistical difficulties in collecting data are some of the reasons for the data gaps. Big data-that is digitally generated, passively produced and automatically collected-offers a great potential for answering some of the data needs. Satellite and sensors, mobile phone call detail records, online transactions and search data, and social media are some of the examples of big data. Integrating big data with the traditional household surveys and administrative data can complement data availability, quality, granularity, accuracy and frequency, and help measure development outcomes temporally and spatially in a number of new ways.The study maps different sources of big data onto development outcomes (based on SDGs) to identify current evidence base, use and the gaps. The map provides a visual overview of existing and ongoing studies. This study also discusses the risks, biases and ethical challenges in using big data for measuring and evaluating development outcomes. The study is a valuable resource for evaluators, researchers, funders, policymakers and practitioners in their effort to contributing to evidence informed policy making and in achieving the SDGs. OBJECTIVES Identify and appraise rigorous impact evaluations (IEs), systematic reviews and the studies that have innovatively used big data to measure any development outcomes with special reference to difficult contexts. SEARCH METHODS A number of general and specialised data bases and reporsitories of organisations were searched using keywords related to big data by an information specialist. SELECTION CRITERIA The studies were selected on basis of whether they used big data sources to measure or evaluate development outcomes. DATA COLLECTION AND ANALYSIS Data collection was conducted using a data extraction tool and all extracted data was entered into excel and then analysed using Stata. The data analysis involved looking at trends and descriptive statistics only. MAIN RESULTS The search yielded over 17,000 records, which we then screened down to 437 studies which became the foundation of our systematic map. We found that overall, there is a sizable and rapidly growing number of measurement studies using big data but a much smaller number of IEs. We also see that the bulk of the big data sources are machine-generated (mostly satellites) represented in the light blue. We find that satellite data was used in over 70% of the measurement studies and in over 80% of the IEs. AUTHORS' CONCLUSIONS This map gives us a sense that there is a lot of work being done to develop appropriate measures using big data which could subsequently be used in IEs. Information on costs, ethics, transparency is lacking in the studies and more work is needed in this area to understand the efficacies related to the use of big data. There are a number of outcomes which are not being studied using big data, either due to the lack to applicability such as education or due to lack of awareness about the new methods and data sources. The map points to a number of gaps as well as opportunities where future researchers can conduct research.
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27
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Citron DT, Guerra CA, García GA, Wu SL, Battle KE, Gibson HS, Smith DL. Quantifying malaria acquired during travel and its role in malaria elimination on Bioko Island. Malar J 2021; 20:359. [PMID: 34461902 PMCID: PMC8404405 DOI: 10.1186/s12936-021-03893-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 08/22/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Malaria elimination is the goal for Bioko Island, Equatorial Guinea. Intensive interventions implemented since 2004 have reduced prevalence, but progress has stalled in recent years. A challenge for elimination has been malaria infections in residents acquired during travel to mainland Equatorial Guinea. The present article quantifies how off-island contributes to remaining malaria prevalence on Bioko Island, and investigates the potential role of a pre-erythrocytic vaccine in making further progress towards elimination. METHODS Malaria transmission on Bioko Island was simulated using a model calibrated based on data from the Malaria Indicator Surveys (MIS) from 2015 to 2018, including detailed travel histories and malaria positivity by rapid-diagnostic tests (RDTs), as well as geospatial estimates of malaria prevalence. Mosquito population density was adjusted to fit local transmission, conditional on importation rates under current levels of control and within-island mobility. The simulations were then used to evaluate the impact of two pre-erythrocytic vaccine distribution strategies: mass treat and vaccinate, and prophylactic vaccination for off-island travellers. Lastly, a sensitivity analysis was performed through an ensemble of simulations fit to the Bayesian joint posterior probability distribution of the geospatial prevalence estimates. RESULTS The simulations suggest that in Malabo, an urban city containing 80% of the population, there are some pockets of residual transmission, but a large proportion of infections are acquired off-island by travellers to the mainland. Outside of Malabo, prevalence was mainly attributable to local transmission. The uncertainty in the local transmission vs. importation is lowest within Malabo and highest outside. Using a pre-erythrocytic vaccine to protect travellers would have larger benefits than using the vaccine to protect residents of Bioko Island from local transmission. In simulations, mass treatment and vaccination had short-lived benefits, as malaria prevalence returned to current levels as the vaccine's efficacy waned. Prophylactic vaccination of travellers resulted in longer-lasting reductions in prevalence. These projections were robust to underlying uncertainty in prevalence estimates. CONCLUSIONS The modelled outcomes suggest that the volume of malaria cases imported from the mainland is a partial driver of continued endemic malaria on Bioko Island, and that continued elimination efforts on must account for human travel activity.
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Affiliation(s)
- Daniel T Citron
- Institute for Health Metrics and Evaluation, University of Washington, Population Health Building/Hans Rosling Center, 3980 15th Ave NE, Seattle, WA, 98195, USA.
| | - Carlos A Guerra
- Medical Care Development International, 8401 Colesville Road Suite 425, Silver Spring, MD, 20910, USA
| | - Guillermo A García
- Medical Care Development International, 8401 Colesville Road Suite 425, Silver Spring, MD, 20910, USA
| | - Sean L Wu
- Division of Epidemiology and Biostatistics, University of California, 2121 Berkeley Way, Berkeley, CA, 94720, USA
| | - Katherine E Battle
- Malaria Atlas Project, Telethon Kids Institute, Perth Children's Hospital, 15 Hospital Avenue, WA, 6009, Nedlands, Australia
- Institute for Disease Modeling, 500 5th Ave N, Seattle, WA, 98109, USA
| | - Harry S Gibson
- Malaria Atlas Project, Telethon Kids Institute, Perth Children's Hospital, 15 Hospital Avenue, WA, 6009, Nedlands, Australia
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Population Health Building/Hans Rosling Center, 3980 15th Ave NE, Seattle, WA, 98195, USA
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28
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Giles JR, Cummings DAT, Grenfell BT, Tatem AJ, zu Erbach-Schoenberg E, Metcalf CJE, Wesolowski A. Trip duration drives shift in travel network structure with implications for the predictability of spatial disease spread. PLoS Comput Biol 2021; 17:e1009127. [PMID: 34375331 PMCID: PMC8378725 DOI: 10.1371/journal.pcbi.1009127] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 08/20/2021] [Accepted: 05/28/2021] [Indexed: 11/19/2022] Open
Abstract
Human travel is one of the primary drivers of infectious disease spread. Models of travel are often used that assume the amount of travel to a specific destination decreases as cost of travel increases with higher travel volumes to more populated destinations. Trip duration, the length of time spent in a destination, can also impact travel patterns. We investigated the spatial patterns of travel conditioned on trip duration and find distinct differences between short and long duration trips. In short-trip duration travel networks, trips are skewed towards urban destinations, compared with long-trip duration networks where travel is more evenly spread among locations. Using gravity models to inform connectivity patterns in simulations of disease transmission, we show that pathogens with shorter generation times exhibit initial patterns of spatial propagation that are more predictable among urban locations. Further, pathogens with a longer generation time have more diffusive patterns of spatial spread reflecting more unpredictable disease dynamics. During an epidemic of an infectious pathogen, cases of disease can be imported to new locations when people travel. The amount of time that an infected person spends in a destination (trip duration) determines how likely they are to infect others while travelling. In this study, we analyzed travel data and found specific spatial patterns in trip duration, where short-duration trips are more common between urban destinations and long-duration trips are evenly spread out among locations. To show how this spatial pattern impacts the spread of infectious diseases, we used data-driven models and simulations to show that pathogens with shorter generation times have patterns of spatial spread that are more predictable among urban locations. However, pathogens with longer generation times tend to spread along the long-duration travel networks that are more evenly distributed among locations giving them more unpredictable disease dynamics.
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Affiliation(s)
- John R. Giles
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- * E-mail:
| | - Derek A. T. Cummings
- Department of Biology and the Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology and the Princeton School of Public and International Affairs, Princeton University, Princeton, New Jersey, United States of America
| | - Andrew J. Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | | | - CJE Metcalf
- Department of Ecology and Evolutionary Biology and the Princeton School of Public and International Affairs, Princeton University, Princeton, New Jersey, United States of America
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
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29
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Persson J, Parie JF, Feuerriegel S. Monitoring the COVID-19 epidemic with nationwide telecommunication data. Proc Natl Acad Sci U S A 2021; 118:e2100664118. [PMID: 34162708 PMCID: PMC8256040 DOI: 10.1073/pnas.2100664118] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
In response to the novel coronavirus disease (COVID-19), governments have introduced severe policy measures with substantial effects on human behavior. Here, we perform a large-scale, spatiotemporal analysis of human mobility during the COVID-19 epidemic. We derive human mobility from anonymized, aggregated telecommunication data in a nationwide setting (Switzerland; 10 February to 26 April 2020), consisting of ∼1.5 billion trips. In comparison to the same time period from 2019, human movement in Switzerland dropped by 49.1%. The strongest reduction is linked to bans on gatherings of more than five people, which are estimated to have decreased mobility by 24.9%, followed by venue closures (stores, restaurants, and bars) and school closures. As such, human mobility at a given day predicts reported cases 7 to 13 d ahead. A 1% reduction in human mobility predicts a 0.88 to 1.11% reduction in daily reported COVID-19 cases. When managing epidemics, monitoring human mobility via telecommunication data can support public decision makers in two ways. First, it helps in assessing policy impact; second, it provides a scalable tool for near real-time epidemic surveillance, thereby enabling evidence-based policies.
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Affiliation(s)
- Joel Persson
- Department of Management, Technology, and Economics, ETH Zurich (Swiss Federal Institute of Technology), 8092 Zurich, Switzerland
| | - Jurriaan F Parie
- Department of Management, Technology, and Economics, ETH Zurich (Swiss Federal Institute of Technology), 8092 Zurich, Switzerland
| | - Stefan Feuerriegel
- Department of Management, Technology, and Economics, ETH Zurich (Swiss Federal Institute of Technology), 8092 Zurich, Switzerland
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30
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The contribution of telco data to fight the COVID-19 pandemic: Experience of Telefonica throughout its footprint. DATA & POLICY 2021. [DOI: 10.1017/dap.2021.6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
Abstract
The COVID-19 pandemic is a global challenge for humanity, in which a large number of resources are invested to develop effective vaccines and treatments. At the same time, governments try to manage the spread of the disease while alleviating the strong impact derived from the slowdown in economic activity. Governments were forced to impose strict lockdown measures to tackle the pandemic. This significantly changed people’s mobility and habits, subsequently impacting the economy. In this context, the availability of tools to effectively monitor and quantify mobility was key for public institutions to decide which policies to implement and for how long. Telefonica has promoted different initiatives to offer governments mobility insights throughout many of the countries where it operates in Europe and Latin America. Mobility indicators with high spatial granularity and frequency of updates were successfully deployed in different formats. However, Telefonica faced many challenges (not only technical) to put these tools into service in a short timing: from reducing latency in insights to ensuring the security and privacy of information. In this article, we provide details on how Telefonica engaged with governments and other stakeholders in different countries as a response to the pandemic. We also cover the challenges faced and the shared learnings from Telefonica’s experience in those countries.
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31
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Valdano E, Okano JT, Colizza V, Mitonga HK, Blower S. Using mobile phone data to reveal risk flow networks underlying the HIV epidemic in Namibia. Nat Commun 2021; 12:2837. [PMID: 33990578 PMCID: PMC8121904 DOI: 10.1038/s41467-021-23051-w] [Citation(s) in RCA: 4] [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: 07/10/2020] [Accepted: 04/08/2021] [Indexed: 12/22/2022] Open
Abstract
Twenty-six million people are living with HIV in sub-Saharan Africa; epidemics are widely dispersed, due to high levels of mobility. However, global elimination strategies do not consider mobility. We use Call Detail Records from 9 billion calls/texts to model mobility in Namibia; we quantify the epidemic-level impact by using a mathematical framework based on spatial networks. We find complex networks of risk flows dispersed risk countrywide: increasing the risk of acquiring HIV in some areas, decreasing it in others. Overall, 40% of risk was mobility-driven. Networks contained multiple risk hubs. All constituencies (administrative units) imported and exported risk, to varying degrees. A few exported very high levels of risk: their residents infected many residents of other constituencies. Notably, prevalence in the constituency exporting the most risk was below average. Large-scale networks of mobility-driven risk flows underlie generalized HIV epidemics in sub-Saharan Africa. In order to eliminate HIV, it is likely to become increasingly important to implement innovative control strategies that focus on disrupting risk flows.
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Affiliation(s)
- Eugenio Valdano
- Center for Biomedical Modeling, The Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Justin T Okano
- Center for Biomedical Modeling, The Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique, IPLESP, Paris, France
| | - Honore K Mitonga
- Department of Epidemiology and Biostatistics, School of Public Health, University of Namibia, Windhoek, Namibia
| | - Sally Blower
- Center for Biomedical Modeling, The Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
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32
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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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Comparing metapopulation dynamics of infectious diseases under different models of human movement. Proc Natl Acad Sci U S A 2021; 118:2007488118. [PMID: 33926962 PMCID: PMC8106338 DOI: 10.1073/pnas.2007488118] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Newly available datasets present exciting opportunities to investigate how human population movement contributes to the spread of infectious diseases across large geographical distances. It is now possible to construct realistic models of infectious disease dynamics for the purposes of understanding global-scale epidemics. Nevertheless, a remaining unanswered question is how best to leverage the new data to parameterize models of movement, and whether one's choice of movement model impacts modeled disease outcomes. We adapt three well-studied models of infectious disease dynamics, the susceptible-infected-recovered model, the susceptible-infected-susceptible model, and the Ross-Macdonald model, to incorporate either of two candidate movement models. We describe the effect that the choice of movement model has on each disease model's results, finding that in all cases, there are parameter regimes where choosing one movement model instead of another has a profound impact on epidemiological outcomes. We further demonstrate the importance of choosing an appropriate movement model using the applied case of malaria transmission and importation on Bioko Island, Equatorial Guinea, finding that one model produces intelligible predictions of R 0, whereas the other produces nonsensical results.
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White LF, Moser CB, Thompson RN, Pagano M. Statistical Estimation of the Reproductive Number From Case Notification Data. Am J Epidemiol 2021; 190:611-620. [PMID: 33034345 DOI: 10.1093/aje/kwaa211] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 09/24/2020] [Accepted: 10/02/2020] [Indexed: 12/20/2022] Open
Abstract
The reproductive number, or reproduction number, is a valuable metric in understanding infectious disease dynamics. There is a large body of literature related to its use and estimation. In the last 15 years, there has been tremendous progress in statistically estimating this number using case notification data. These approaches are appealing because they are relevant in an ongoing outbreak (e.g., for assessing the effectiveness of interventions) and do not require substantial modeling expertise to be implemented. In this article, we describe these methods and the extensions that have been developed. We provide insight into the distinct interpretations of the estimators proposed and provide real data examples to illustrate how they are implemented. Finally, we conclude with a discussion of available software and opportunities for future development.
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35
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Ciavarella C, Ferguson NM. Deriving fine-scale models of human mobility from aggregated origin-destination flow data. PLoS Comput Biol 2021; 17:e1008588. [PMID: 33571187 PMCID: PMC7920350 DOI: 10.1371/journal.pcbi.1008588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 03/01/2021] [Accepted: 12/01/2020] [Indexed: 11/18/2022] Open
Abstract
The spatial dynamics of epidemics are fundamentally affected by patterns of human mobility. Mobile phone call detail records (CDRs) are a rich source of mobility data, and allow semi-mechanistic models of movement to be parameterised even for resource-poor settings. While the gravity model typically reproduces human movement reasonably well at the administrative level spatial scale, past studies suggest that parameter estimates vary with the level of spatial discretisation at which models are fitted. Given that privacy concerns usually preclude public release of very fine-scale movement data, such variation would be problematic for individual-based simulations of epidemic spread parametrised at a fine spatial scale. We therefore present new methods to fit fine-scale mathematical mobility models (here we implement variants of the gravity and radiation models) to spatially aggregated movement data and investigate how model parameter estimates vary with spatial resolution. We use gridded population data at 1km resolution to derive population counts at different spatial scales (down to ∼ 5km grids) and implement mobility models at each scale. Parameters are estimated from administrative-level flow data between overnight locations in Kenya and Namibia derived from CDRs: where the model spatial resolution exceeds that of the mobility data, we compare the flow data between a particular origin and destination with the sum of all model flows between cells that lie within those particular origin and destination administrative units. Clear evidence of over-dispersion supports the use of negative binomial instead of Poisson likelihood for count data with high values. Radiation models use fewer parameters than the gravity model and better predict trips between overnight locations for both considered countries. Results show that estimates for some parameters change between countries and with spatial resolution and highlight how imperfect flow data and spatial population distribution can influence model fit. The growing use of large-scale individual-based models calls for reliable modelling of human population movement at ever finer scales. Mobility models have at times been fit to fine-scale movement data, such as travel questionnaires and GPS data. However, the restricted size of such datasets make them suboptimal for parametrising large-scale simulations. Larger datasets, such as census commuting data or mobile phone data, pose a different problem in that such datasets are usually made available at a much coarser spatial resolution than required for individual-based simulations. Here we present a straightforward, if computationally intensive, method to obtain fine-scale movement estimates from coarse-scale movement data. We trial the method on movement data from Kenya and Namibia and implement two of the most common mathematical mobility models, the gravity and the radiation models. Our findings confirm previous research that the parameter estimates for the mobility models differ across spatial scales and countries. We also investigate how population spatial distribution and the characteristics of the flow datasets influence parameter estimates.
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Affiliation(s)
- Constanze Ciavarella
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London
- * E-mail:
| | - Neil M. Ferguson
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London
- The Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London
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36
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Chang MC, Kahn R, Li YA, Lee CS, Buckee CO, Chang HH. Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan. BMC Public Health 2021; 21:226. [PMID: 33504339 PMCID: PMC7838857 DOI: 10.1186/s12889-021-10260-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 01/17/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases. METHODS In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. RESULTS We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. CONCLUSIONS To prepare for the potential spread within Taiwan, we utilized Facebook's aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.
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Affiliation(s)
- Meng-Chun Chang
- Department of Life Science & Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan
| | - Rebecca Kahn
- Department of Epidemiology & the Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yu-An Li
- Department of Life Science & Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan
| | - Cheng-Sheng Lee
- Department of Life Science & Institute of Molecular and Cellular Biology, National Tsing Hua University, Hsinchu, Taiwan
| | - Caroline O Buckee
- Department of Epidemiology & the Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Hsiao-Han Chang
- Department of Life Science & Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan.
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37
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Potgieter A, Fabris-Rotelli IN, Kimmie Z, Dudeni-Tlhone N, Holloway JP, Janse van Rensburg C, Thiede RN, Debba P, Manjoo-Docrat R, Abdelatif N, Khuluse-Makhanya S. Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa. Front Big Data 2021. [PMID: 34746771 DOI: 10.20944/preprints202106.0211.v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023] Open
Abstract
The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data sources convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices at a variety of spatial resolutions and further compares the results through hierarchical clustering. We consider four methods for constructing spatial weight matrices representing mobility between spatial units, taking into account distance between spatial units as well as spatial covariates. This provides insight for the user into which data provides what type of information and in what situations a particular data source is most useful.
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Affiliation(s)
- A Potgieter
- Department of Statistics, University of Pretoria, Pretoria, South Africa
| | - I N Fabris-Rotelli
- Department of Statistics, University of Pretoria, Pretoria, South Africa
| | - Z Kimmie
- Foundation of Human Rights, Johannesburg, South Africa
| | - N Dudeni-Tlhone
- Operational Intelligence, NextGen Enterprises and Institutions, Council for Scientific and Industrial Research, Pretoria, South Africa
| | - J P Holloway
- Operational Intelligence, NextGen Enterprises and Institutions, Council for Scientific and Industrial Research, Pretoria, South Africa
| | - C Janse van Rensburg
- Biostatistics Research Unit, South African Medical Research Council, Cape Town, South Africa
| | - R N Thiede
- Department of Statistics, University of Pretoria, Pretoria, South Africa
| | - P Debba
- Inclusive Smart Settlements and Regions, Smart Places, Council for Scientific and Industrial Research, Pretoria, South Africa
- Department of Statistics and Actuarial Science, University of Witwatersrand, Johannesburg, South Africa
| | - R Manjoo-Docrat
- Department of Statistics and Actuarial Science, University of Witwatersrand, Johannesburg, South Africa
| | - N Abdelatif
- Biostatistics Research Unit, South African Medical Research Council, Cape Town, South Africa
| | - S Khuluse-Makhanya
- IBM Research, Johannesburg, South Africa
- College of Graduate Studies, University of South Africa, Johannesburg, South Africa
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38
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Buyon LE, Santamaria AM, Early AM, Quijada M, Barahona I, Lasso J, Avila M, Volkman SK, Marti M, Neafsey DE, Obaldia III N. Population genomics of Plasmodium vivax in Panama to assess the risk of case importation on malaria elimination. PLoS Negl Trop Dis 2020; 14:e0008962. [PMID: 33315861 PMCID: PMC7769613 DOI: 10.1371/journal.pntd.0008962] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 12/28/2020] [Accepted: 11/06/2020] [Indexed: 12/13/2022] Open
Abstract
Malaria incidence in Panama has plateaued in recent years in spite of elimination efforts, with almost all cases caused by Plasmodium vivax. Notwithstanding, overall malaria prevalence remains low (fewer than 1 case per 1000 persons). We used selective whole genome amplification to sequence 59 P. vivax samples from Panama. The P. vivax samples were collected from two periods (2007-2009 and 2017-2019) to study the population structure and transmission dynamics of the parasite. Imported cases resulting from increased levels of human migration could threaten malaria elimination prospects, and four of the samples evaluated came from individuals with travel history. We explored patterns of recent common ancestry among the samples and observed that a highly genetically related lineage (termed CL1) was dominant among the samples (47 out of 59 samples with good sequencing coverage), spanning the entire period of the collection (2007-2019) and all regions of the country. We also found a second, smaller clonal lineage (termed CL2) of four parasites collected between 2017 and 2019. To explore the regional context of Panamanian P. vivax we conducted principal components analysis and constructed a neighbor-joining tree using these samples and samples collected worldwide from a previous study. Three of the four samples with travel history clustered with samples collected from their suspected country of origin (consistent with importation), while one appears to have been a result of local transmission. The small number of Panamanian P. vivax samples not belonging to either CL1 or CL2 clustered with samples collected from Colombia, suggesting they represent the genetically similar ancestral P. vivax population in Panama or were recently imported from Colombia. The low diversity we observe in Panama indicates that this parasite population has been previously subject to a severe bottleneck and may be eligible for elimination. Additionally, while we confirmed that P. vivax is imported to Panama from diverse geographic locations, the lack of impact from imported cases on the overall parasite population genomic profile suggests that onward transmission from such cases is limited and that imported cases may not presently pose a major barrier to elimination.
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Affiliation(s)
- Lucas E. Buyon
- Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | | | - Angela M. Early
- Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Mario Quijada
- Instituto Conmemorativo Gorgas de Estudios de la Salud, Panama City, Panama
| | | | | | | | - Sarah K. Volkman
- Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
- Simmons University, College of Natural, Behavioral and Health Sciences, Boston, Massachusetts, United States of America
| | | | - Daniel E. Neafsey
- Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
- * E-mail: (DEN); (NO)
| | - Nicanor Obaldia III
- Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
- Instituto Conmemorativo Gorgas de Estudios de la Salud, Panama City, Panama
- University of Glasgow, Glasgow, United Kingdom
- * E-mail: (DEN); (NO)
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39
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Searle KM, Katowa B, Musonda M, Pringle JC, Hamapumbu H, Matoba J, Lubinda M, Shields T, Kobayashi T, Stevenson JC, Norris DE, Thuma PE, Wesolowski A, Moss WJ, For The Southern And Central Africa International Center Of Excellence For Malaria Research. Sustained Malaria Transmission despite Reactive Screen-and-Treat in a Low-Transmission Area of Southern Zambia. Am J Trop Med Hyg 2020; 104:671-679. [PMID: 33236715 PMCID: PMC7866307 DOI: 10.4269/ajtmh.20-0947] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 09/28/2020] [Indexed: 12/30/2022] Open
Abstract
Malaria elimination strategies are designed to more effectively identify and treat infected individuals to interrupt transmission. One strategy, reactive screen-and-treat, starts with passive detection of symptomatic cases at health facilities. Individuals residing within the index case and neighboring households are screened with a malaria rapid diagnostic test (RDT) and treated if positive. However, it is unclear to what extent this strategy is effective in reducing transmission. Reactive screen-and-treat was implemented in Choma district, Southern Province, Zambia, in 2013, in which residents of the index case and neighboring households within 140 m were screened with an RDT. From March 2016 to July 2018, the screening radius was extended to 250-m, and additional follow-up visits at 30 and 90 days were added to evaluate the strategy. Plasmodium falciparum parasite prevalence was measured using an RDT and by quantitative PCR (qPCR). A 24-single nucleotide polymorphism molecular bar-code assay was used to genotype parasites. Eighty-four index case households with 676 residents were enrolled between March 2016 and March 2018. Within each season, parasite prevalence declined significantly in index households at the 30-day visit and remained low at the 90-day visit. However, parasite prevalence was not reduced to zero. Infections identified by qPCR persisted between study visits and were not identified by RDT. Parasites identified within the same household were most genetically related; however, overall parasite relatedness was low and similar across time and space. Thus, despite implementation of a reactive screen-and-treat program, parasitemia was not eliminated, and persisted in targeted households for at least 3 months.
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Affiliation(s)
- Kelly M Searle
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, Minnesota
| | | | | | - Julia C Pringle
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | | | | | - Timothy Shields
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Tamaki Kobayashi
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Jennifer C Stevenson
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.,Macha Research Trust, Macha, Zambia
| | - Douglas E Norris
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Philip E Thuma
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.,Macha Research Trust, Macha, Zambia
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - William J Moss
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.,W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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40
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Milusheva S. Managing the spread of disease with mobile phone data. JOURNAL OF DEVELOPMENT ECONOMICS 2020; 147:102559. [PMID: 33144750 PMCID: PMC7561616 DOI: 10.1016/j.jdeveco.2020.102559] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 08/19/2020] [Accepted: 08/20/2020] [Indexed: 06/04/2023]
Abstract
While human mobility has important benefits for economic growth, it can generate negative externalities. This paper studies the effect of mobility on the spread of disease in a low-incidence setting when people do not internalize their risks to others. Using malaria as a case study and 15 billion mobile phone records across nine million SIM cards, this paper quantifies the relationship between travel and the spread of disease. The estimates indicate that an infected traveler contributes to 1.66 additional cases reported in the health facility at the traveler's destination. This paper develops a simulation-based policy tool that uses mobile phone data to inform strategic targeting of travelers based on their origins and destinations. The simulations suggest that targeting informed by mobile phone data could reduce the caseload by 50 percent more than current strategies that rely only on previous incidence.
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41
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Grantz KH, Meredith HR, Cummings DAT, Metcalf CJE, Grenfell BT, Giles JR, Mehta S, Solomon S, Labrique A, Kishore N, Buckee CO, Wesolowski A. The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology. Nat Commun 2020; 11:4961. [PMID: 32999287 PMCID: PMC7528106 DOI: 10.1038/s41467-020-18190-5] [Citation(s) in RCA: 151] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 08/06/2020] [Indexed: 11/24/2022] Open
Abstract
The ongoing coronavirus disease 2019 (COVID-19) pandemic has heightened discussion of the use of mobile phone data in outbreak response. Mobile phone data have been proposed to monitor effectiveness of non-pharmaceutical interventions, to assess potential drivers of spatiotemporal spread, and to support contact tracing efforts. While these data may be an important part of COVID-19 response, their use must be considered alongside a careful understanding of the behaviors and populations they capture. Here, we review the different applications for mobile phone data in guiding and evaluating COVID-19 response, the relevance of these applications for infectious disease transmission and control, and potential sources and implications of selection bias in mobile phone data. We also discuss best practices and potential pitfalls for directly integrating the collection, analysis, and interpretation of these data into public health decision making.
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Affiliation(s)
- Kyra H Grantz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Hannah R Meredith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Derek A T Cummings
- Department of Biology and the Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology and the Woodrow Wilson School of International and Public Affairs, Princeton University, Princeton, NJ, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology and the Woodrow Wilson School of International and Public Affairs, Princeton University, Princeton, NJ, USA
| | - John R Giles
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Shruti Mehta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sunil Solomon
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Alain Labrique
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Nishant Kishore
- Department of Epidemiology and the Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Caroline O Buckee
- Department of Epidemiology and the Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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42
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Carrasco-Escobar G, Fornace K, Wong D, Padilla-Huamantinco PG, Saldaña-Lopez JA, Castillo-Meza OE, Caballero-Andrade AE, Manrique E, Ruiz-Cabrejos J, Barboza JL, Rodriguez H, Henostroza G, Gamboa D, Castro MC, Vinetz JM, Llanos-Cuentas A. Open-Source 3D Printable GPS Tracker to Characterize the Role of Human Population Movement on Malaria Epidemiology in River Networks: A Proof-of-Concept Study in the Peruvian Amazon. Front Public Health 2020; 8:526468. [PMID: 33072692 PMCID: PMC7542225 DOI: 10.3389/fpubh.2020.526468] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 08/21/2020] [Indexed: 11/13/2022] Open
Abstract
Human movement affects malaria epidemiology at multiple geographical levels; however, few studies measure the role of human movement in the Amazon Region due to the challenging conditions and cost of movement tracking technologies. We developed an open-source low-cost 3D printable GPS-tracker and used this technology in a cohort study to characterize the role of human population movement in malaria epidemiology in a rural riverine village in the Peruvian Amazon. In this pilot study of 20 participants (mean age = 40 years old), 45,980 GPS coordinates were recorded over 1 month. Characteristic movement patterns were observed relative to the infection status and occupation of the participants. Applying two analytical animal movement ecology methods, utilization distributions (UDs) and integrated step selection functions (iSSF), we showed contrasting environmental selection and space use patterns according to infection status. These data suggested an important role of human movement in the epidemiology of malaria in the Peruvian Amazon due to high connectivity between villages of the same riverine network, suggesting limitations of current community-based control strategies. We additionally demonstrate the utility of this low-cost technology with movement ecology analysis to characterize human movement in resource-poor environments.
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Affiliation(s)
- Gabriel Carrasco-Escobar
- Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru.,Division of Infectious Diseases, Department of Medicine, University of California, San Diego, La Jolla, CA, United States.,Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Kimberly Fornace
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Daniel Wong
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Pierre G Padilla-Huamantinco
- Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru.,Departamento de Ingenieria, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Jose A Saldaña-Lopez
- Departamento de Ingenieria, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Ober E Castillo-Meza
- Departamento de Ingenieria, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Armando E Caballero-Andrade
- Departamento de Ingenieria, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Edgar Manrique
- Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru.,Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Jorge Ruiz-Cabrejos
- Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru.,Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Jose Luis Barboza
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | | | - German Henostroza
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Dionicia Gamboa
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru.,Departamento de Ciencias Celulares y Moleculares, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru.,Instituto de Medicinal Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Marcia C Castro
- Department of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - Joseph M Vinetz
- Instituto de Medicinal Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru.,Section of Infectious Diseases, Yale School of Medicine, New Haven, CT, United States
| | - Alejandro Llanos-Cuentas
- Instituto de Medicinal Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru.,Facultad de Salud Pública y Administración, Universidad Peruana Cayetano Heredia, Lima, Peru
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43
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Ruktanonchai NW, Floyd JR, Lai S, Ruktanonchai CW, Sadilek A, Rente-Lourenco P, Ben X, Carioli A, Gwinn J, Steele JE, Prosper O, Schneider A, Oplinger A, Eastham P, Tatem AJ. Assessing the impact of coordinated COVID-19 exit strategies across Europe. Science 2020; 369:1465-1470. [PMID: 32680881 PMCID: PMC7402626 DOI: 10.1126/science.abc5096] [Citation(s) in RCA: 118] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 07/13/2020] [Indexed: 12/25/2022]
Abstract
As rates of new coronavirus disease 2019 (COVID-19) cases decline across Europe owing to nonpharmaceutical interventions such as social distancing policies and lockdown measures, countries require guidance on how to ease restrictions while minimizing the risk of resurgent outbreaks. We use mobility and case data to quantify how coordinated exit strategies could delay continental resurgence and limit community transmission of COVID-19. We find that a resurgent continental epidemic could occur as many as 5 weeks earlier when well-connected countries with stringent existing interventions end their interventions prematurely. Further, we find that appropriate coordination can greatly improve the likelihood of eliminating community transmission throughout Europe. In particular, synchronizing intermittent lockdowns across Europe means that half as many lockdown periods would be required to end continent-wide community transmission.
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Affiliation(s)
- N W Ruktanonchai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.
- Population Health Sciences, Virginia Tech, Blacksburg, VA, USA
| | - J R Floyd
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.
| | - S Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.
| | - C W Ruktanonchai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | | | | | - X Ben
- Google, Mountain View, CA, USA
| | - A Carioli
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - J Gwinn
- College of Public Health, University of Kentucky, Lexington, KY, USA
| | - J E Steele
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - O Prosper
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
| | | | | | | | - A J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
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44
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Giles JR, Zu Erbach-Schoenberg E, Tatem AJ, Gardner L, Bjørnstad ON, Metcalf CJE, Wesolowski A. The duration of travel impacts the spatial dynamics of infectious diseases. Proc Natl Acad Sci U S A 2020; 117:22572-22579. [PMID: 32839329 PMCID: PMC7486699 DOI: 10.1073/pnas.1922663117] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Humans can impact the spatial transmission dynamics of infectious diseases by introducing pathogens into susceptible environments. The rate at which this occurs depends in part on human-mobility patterns. Increasingly, mobile-phone usage data are used to quantify human mobility and investigate the impact on disease dynamics. Although the number of trips between locations and the duration of those trips could both affect infectious-disease dynamics, there has been limited work to quantify and model the duration of travel in the context of disease transmission. Using mobility data inferred from mobile-phone calling records in Namibia, we calculated both the number of trips between districts and the duration of these trips from 2010 to 2014. We fit hierarchical Bayesian models to these data to describe both the mean trip number and duration. Results indicate that trip duration is positively related to trip distance, but negatively related to the destination population density. The highest volume of trips and shortest trip durations were among high-density districts, whereas trips among low-density districts had lower volume with longer duration. We also analyzed the impact of including trip duration in spatial-transmission models for a range of pathogens and introduction locations. We found that inclusion of trip duration generally delays the rate of introduction, regardless of pathogen, and that the variance and uncertainty around spatial spread increases proportionally with pathogen-generation time. These results enhance our understanding of disease-dispersal dynamics driven by human mobility, which has potential to elucidate optimal spatial and temporal scales for epidemic interventions.
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Affiliation(s)
- John R Giles
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205;
| | - Elisabeth Zu Erbach-Schoenberg
- Department of Geography and the Environment, University of Southampton, Southampton SO17 1BJ, United Kingdom
- WorldPop, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Andrew J Tatem
- Department of Geography and the Environment, University of Southampton, Southampton SO17 1BJ, United Kingdom
- WorldPop, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Lauren Gardner
- Department of Civil and Systems Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, MD 21218
| | - Ottar N Bjørnstad
- Department of Entomology, Pennsylvania State University, University Park, PA 16802
| | - C J E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08544
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205
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45
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Chang MC, Kahn R, Li YA, Lee CS, Buckee CO, Chang HH. Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.04.07.20053439. [PMID: 32817972 PMCID: PMC7430617 DOI: 10.1101/2020.04.07.20053439] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Background As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases. Methods In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. Results We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. Conclusions To prepare for the potential spread within Taiwan, we utilized Facebook's aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.
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Affiliation(s)
- Meng-Chun Chang
- Department of Life Science & Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan
| | - Rebecca Kahn
- Department of Epidemiology & the Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yu-An Li
- Department of Life Science & Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan
| | - Cheng-Sheng Lee
- Department of Life Science & Institute of Molecular and Cellular Biology, National Tsing Hua University, Hsinchu, Taiwan
| | - Caroline O. Buckee
- Department of Epidemiology & the Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Hsiao-Han Chang
- Department of Life Science & Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan
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46
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Dieng S, Ba EH, Cissé B, Sallah K, Guindo A, Ouedraogo B, Piarroux M, Rebaudet S, Piarroux R, Landier J, Sokhna C, Gaudart J. Spatio-temporal variation of malaria hotspots in Central Senegal, 2008-2012. BMC Infect Dis 2020; 20:424. [PMID: 32552759 PMCID: PMC7301493 DOI: 10.1186/s12879-020-05145-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 06/10/2020] [Indexed: 12/01/2022] Open
Abstract
Background In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal and to identify the meteorological, environmental, and preventive factors that influence this variation. Methods This study analysed the weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population approximately 500,000) as part of a trial of seasonal malaria chemoprevention (SMC). Data on weekly rainfall and annual vegetation types were obtained for each village through remote sensing. The time series of weekly malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected during each transmission period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model. Results The malaria incidence for the entire area varied between 0 and 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR = 0.48, IC95%: 0.33–0.68). The association between rainfall and hotspot status was non-linear and depended on both vegetation type and amount of rainfall. The association between village location in the study area and hotspot status was also shown. Conclusion In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. By taking into consideration the environmental and meteorological characteristics common to all hotspots, monitoring of these factors could lead targeted public health interventions at the local level. Moreover, spatial hotspots and foci of malaria persisting during LTPs need to be further addressed. Trial registration The data used in this work were obtained from a clinical trial registered on July 10, 2008 at www.clinicaltrials.gov under NCT00712374.
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Affiliation(s)
- Sokhna Dieng
- Aix Marseille Univ, IRD, INSERM, SESSTIM, Marseille, France. .,Ecole des Hautes Etudes en Santé Publique, Rennes, France.
| | - El Hadj Ba
- UMR VITROME, Campus International IRD-UCAD de l'IRD, Dakar, Sénégal
| | - Badara Cissé
- Institut de Recherche en Santé, de Surveillance Épidémiologique et de Formation (IRESSEF) Diamniadio, Dakar, Sénégal
| | - Kankoe Sallah
- Aix Marseille Univ, IRD, INSERM, SESSTIM, Marseille, France.,AP-HP, Hôpital Bichat, Unité de Recherche Clinique PNVS, Paris, France
| | - Abdoulaye Guindo
- Aix Marseille Univ, IRD, INSERM, SESSTIM, Marseille, France.,Research and Training Center - Ogobara K Doumbo, FMOS-FAPH, Mali-NIAID-ICER, Université des Sciences, des Techniques et des Technologies de Bamako, Bamako, Mali
| | - Boukary Ouedraogo
- Aix Marseille Univ, IRD, INSERM, SESSTIM, Marseille, France.,Direction des Systèmes d'Information en santé, Ministère de la santé, Ouagadougou, Burkina Faso
| | - Martine Piarroux
- French Armed Forces Center for Epidemiology and Public Health (CESPA), Marseille, France
| | - Stanislas Rebaudet
- APHM, Assistance Publique - Hôpitaux de Marseille, Marseille, France.,Hôpital Européen, Marseille, France
| | - Renaud Piarroux
- Sorbonne Université, INSERM, Institut Pierre-Louis d'Epidémiologie et de Santé Publique, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Jordi Landier
- Aix Marseille Univ, IRD, INSERM, SESSTIM, Marseille, France
| | - Cheikh Sokhna
- UMR VITROME, Campus International IRD-UCAD de l'IRD, Dakar, Sénégal
| | - Jean Gaudart
- Aix Marseille Univ, APHM, INSERM, IRD, SESSTIM, Hop Timone, BioSTIC, Biostatistic & ICT, Marseille, France
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47
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Kraemer MUG, Sadilek A, Zhang Q, Marchal NA, Tuli G, Cohn EL, Hswen Y, Perkins TA, Smith DL, Reiner RC, Brownstein JS. Mapping global variation in human mobility. Nat Hum Behav 2020; 4:800-810. [PMID: 32424257 DOI: 10.1038/s41562-020-0875-0] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Accepted: 03/30/2020] [Indexed: 01/11/2023]
Abstract
The geographic variation of human movement is largely unknown, mainly due to a lack of accurate and scalable data. Here we describe global human mobility patterns, aggregated from over 300 million smartphone users. The data cover nearly all countries and 65% of Earth's populated surface, including cross-border movements and international migration. This scale and coverage enable us to develop a globally comprehensive human movement typology. We quantify how human movement patterns vary across sociodemographic and environmental contexts and present international movement patterns across national borders. Fitting statistical models, we validate our data and find that human movement laws apply at 10 times shorter distances and movement declines 40% more rapidly in low-income settings. These results and data are made available to further understanding of the role of human movement in response to rapid demographic, economic and environmental changes.
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Affiliation(s)
- Moritz 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.
| | | | - Qian Zhang
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Gaurav Tuli
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA
| | - Emily L Cohn
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA
| | - Yulin Hswen
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA.,Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - T Alex Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA. .,Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA.
| | - John S Brownstein
- Harvard Medical School, Harvard University, Boston, MA, USA. .,Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA.
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48
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Abstract
Costa Rica is near malaria elimination. This achievement has followed shifts in malaria health policy. Here, we evaluate the impacts that different health policies have had on malaria transmission in Costa Rica from 1913 to 2018. We identified regime shifts and used regression models to measure the impact of different health policies on malaria transmission in Costa Rica using annual case records. We found that vector control and prophylactic treatments were associated with a 50% malaria case reduction in 1929-1931 compared with 1913-1928. DDT introduction in 1946 was associated with an increase in annual malaria case reduction from 7.6% (1942-1946) to 26.4% (1947-1952). The 2006 introduction of 7-day supervised chloroquine and primaquine treatments was the most effective health policy between 1957 and 2018, reducing annual malaria cases by 98% (2009-2018) when compared with 1957-1968. We also found that effective malaria reduction policies have been sensitive to natural catastrophes and extreme climatic events, both of which have increased malaria transmission in Costa Rica. Currently, outbreaks follow malaria importation into vulnerable areas of Costa Rica. This highlights the need to timely diagnose and treat malaria, while improving living standards, in the affected areas.
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49
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Pollard EJM, MacLaren D, Russell TL, Burkot TR. Protecting the peri-domestic environment: the challenge for eliminating residual malaria. Sci Rep 2020; 10:7018. [PMID: 32341476 PMCID: PMC7184721 DOI: 10.1038/s41598-020-63994-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 04/07/2020] [Indexed: 11/09/2022] Open
Abstract
Malaria transmission after universal access and use of malaria preventive services is known as residual malaria transmission. The concurrent spatial-temporal distributions of people and biting mosquitoes in malaria endemic villages determines where and when residual malaria transmission occurs. Understanding human and vector population behaviors and movements is a critical first step to prevent mosquito bites to eliminate residual malaria transmission. This study identified where people in the Solomon Islands are over 24-hour periods. Participants (59%) were predominantly around the house but not in their house when most biting by Anopheles farauti, the dominant malaria vector, occurs. While 84% of people slept under a long-lasting insecticide-treated bed net (LLIN), on average only 7% were under an LLIN during the 18:00 to 21:00 h peak mosquito biting period. On average, 34% of participants spend at least one night away from their homes each fortnight. Despite high LLIN use while sleeping, most human biting by An. farauti occurs early in the evening before people go to sleep when people are in peri-domestic areas (predominantly on verandas or in kitchen areas). Novel vector control tools that protect individuals from mosquito bites between sundown and when people sleep are needed for peri-domestic areas.
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Affiliation(s)
- Edgar J M Pollard
- James Cook University, Australian Institute of Tropical Health and Medicine, Cairns, QLD 4870, Australia.
| | - David MacLaren
- James Cook University, Australian Institute of Tropical Health and Medicine, Cairns, QLD 4870, Australia
| | - Tanya L Russell
- James Cook University, Australian Institute of Tropical Health and Medicine, Cairns, QLD 4870, Australia
| | - Thomas R Burkot
- James Cook University, Australian Institute of Tropical Health and Medicine, Cairns, QLD 4870, Australia.
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50
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Sinha I, Sayeed AA, Uddin D, Wesolowski A, Zaman SI, Faiz MA, Ghose A, Rahman MR, Islam A, Karim MJ, Saha A, Rezwan MK, Shamsuzzaman AKM, Jhora ST, Aktaruzzaman MM, Chang HH, Miotto O, Kwiatkowski D, Dondorp AM, Day NPJ, Hossain MA, Buckee C, Maude RJ. Mapping the travel patterns of people with malaria in Bangladesh. BMC Med 2020; 18:45. [PMID: 32127002 PMCID: PMC7055101 DOI: 10.1186/s12916-020-1512-5] [Citation(s) in RCA: 7] [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: 07/04/2019] [Accepted: 02/05/2020] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Spread of malaria and antimalarial resistance through human movement present major threats to current goals to eliminate the disease. Bordering the Greater Mekong Subregion, southeast Bangladesh is a potentially important route of spread to India and beyond, but information on travel patterns in this area are lacking. METHODS Using a standardised short survey tool, 2090 patients with malaria were interviewed at 57 study sites in 2015-2016 about their demographics and travel patterns in the preceding 2 months. RESULTS Most travel was in the south of the study region between Cox's Bazar district (coastal region) to forested areas in Bandarban (31% by days and 45% by nights), forming a source-sink route. Less than 1% of travel reported was between the north and south forested areas of the study area. Farmers (21%) and students (19%) were the top two occupations recorded, with 67 and 47% reporting travel to the forest respectively. Males aged 25-49 years accounted for 43% of cases visiting forests but only 24% of the study population. Children did not travel. Women, forest dwellers and farmers did not travel beyond union boundaries. Military personnel travelled the furthest especially to remote forested areas. CONCLUSIONS The approach demonstrated here provides a framework for identifying key traveller groups and their origins and destinations of travel in combination with knowledge of local epidemiology to inform malaria control and elimination efforts. Working with the NMEP, the findings were used to derive a set of policy recommendations to guide targeting of interventions for elimination.
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Affiliation(s)
- Ipsita Sinha
- Mahidol-Oxford Tropical Medicine Research Unit, Bangkok, Thailand.
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
| | | | - Didar Uddin
- Mahidol-Oxford Tropical Medicine Research Unit, Bangkok, Thailand
| | - Amy Wesolowski
- John Hopkins Bloomberg School of Public Health, Baltimore, USA
- Harvard T.H. Chan School of Public Health, Harvard University, Boston, USA
| | - Sazid Ibna Zaman
- Mahidol-Oxford Tropical Medicine Research Unit, Bangkok, Thailand
- BRAC (Building Resources Across Communities), BRAC Centre, Mohakhali, Dhaka, Bangladesh
| | - M Abul Faiz
- Mahidol-Oxford Tropical Medicine Research Unit, Bangkok, Thailand
- Dev Care Foundation, Dhaka, Bangladesh
| | - Aniruddha Ghose
- Chittagong Medical College and Hospital, Chittagong, Bangladesh
| | | | - Akramul Islam
- BRAC (Building Resources Across Communities), BRAC Centre, Mohakhali, Dhaka, Bangladesh
| | - Mohammad Jahirul Karim
- Mahidol-Oxford Tropical Medicine Research Unit, Bangkok, Thailand
- Communicable Disease Control, Directorate General of Health Services, Dhaka, Bangladesh
- Filariasis Elimination, STH Control, Dhaka, Bangladesh
| | - Anjan Saha
- National Malaria Elimination Programme, Dhaka, Bangladesh
| | - M Kamar Rezwan
- Vector-Borne Disease Control, World Health Organization, Dhaka, Bangladesh
| | | | - Sanya Tahmina Jhora
- Communicable Disease Control, Directorate General of Health Services, Dhaka, Bangladesh
| | - M M Aktaruzzaman
- Communicable Disease Control, Directorate General of Health Services, Dhaka, Bangladesh
- National Malaria Elimination Programme, Dhaka, Bangladesh
| | - Hsiao-Han Chang
- Harvard T.H. Chan School of Public Health, Harvard University, Boston, USA
| | - Olivo Miotto
- Mahidol-Oxford Tropical Medicine Research Unit, Bangkok, Thailand
- Big Data Institute, University of Oxford, Oxford, UK
| | - Dominic Kwiatkowski
- Big Data Institute, University of Oxford, Oxford, UK
- Wellcome Sanger Institute, Hinxton, Cambridge, UK
| | - Arjen M Dondorp
- Mahidol-Oxford Tropical Medicine Research Unit, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Nicholas P J Day
- Mahidol-Oxford Tropical Medicine Research Unit, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - M Amir Hossain
- Chittagong Medical College and Hospital, Chittagong, Bangladesh
| | - Caroline Buckee
- Harvard T.H. Chan School of Public Health, Harvard University, Boston, USA
| | - Richard J Maude
- Mahidol-Oxford Tropical Medicine Research Unit, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Harvard T.H. Chan School of Public Health, Harvard University, Boston, USA
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