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Bajerge NM, Khankeh H, Dashtbozorgi A, Farrokhi M. Abstruse Side of Climate Change, Impact on Malaria: A Systematic Evidence Review Comparing Iran versus Globally. IRANIAN JOURNAL OF PUBLIC HEALTH 2024; 53:1047-1057. [PMID: 38912133 PMCID: PMC11188642 DOI: 10.18502/ijph.v53i5.15584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 09/16/2023] [Indexed: 06/25/2024]
Abstract
Background Infectious outbreaks due to disrupted social and environmental conditions after climate change-induced events complicate disasters. This research aimed to determine the contentions of bioclimatic variables and extreme events on the prevalence of the most common Climate-Sensitive Infectious Disease (CSID); Malaria in Iran. Methods The present narrative systematic review study was conducted on the bioclimatic variable impact on the prevalence of malaria, as a common CSID. The search was conducted in 3 sections: global climate change-related studies, disaster related, and studies that were conducted in Iran. The literature search was focused on papers published in English and Persian from Mar 2000 to Dec 2021, using electronic databases; Scopus, Web of Science, PubMed, Google Scholar, SID, Magiran, and IranDoc. Results Overall, 41 studies met the inclusion criteria. The various types of climatic variables including; Temperature, rainfall, relative humidity, and hydrological events including; flood, drought, and cyclones has been reported as a predictor of malaria. The results of studies, inappropriately and often were inconsistent in both Iran and other parts of the world. Conclusion Identifying malaria outbreak risks is essential to assess vulnerability, and a starting point to identify where the health system is required to reduce the vulnerability and exposure of the population. The finding of most related studies is not congruent to achieve reliable information, more extensive studies in all climates and regions of the country, by climatic models and high accuracy risk map, using the long period of bioclimatic variables and malaria trend is recommended.
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Affiliation(s)
- Nader Majidi Bajerge
- Health in Emergency and Disaster Research Center, Social Health Research Institute, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
| | - Hamidreza Khankeh
- Health in Emergency and Disaster Research Center, Social Health Research Institute, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
| | - Amene Dashtbozorgi
- Center for Remote Sensing and GIS Research, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
| | - Mehrdad Farrokhi
- Health in Emergency and Disaster Research Center, Social Health Research Institute, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
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2
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Nelder MP, Schats R, Poinar HN, Cooke A, Brickley MB. Pathogen prospecting of museums: Reconstructing malaria epidemiology. Proc Natl Acad Sci U S A 2024; 121:e2310859121. [PMID: 38527214 PMCID: PMC11009618 DOI: 10.1073/pnas.2310859121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024] Open
Abstract
Malaria is a disease of global significance. Ongoing changes to the earth's climate, antimalarial resistance, insecticide resistance, and socioeconomic decline test the resilience of malaria prevention programs. Museum insect specimens present an untapped resource for studying vector-borne pathogens, spurring the question: Do historical mosquito collections contain Plasmodium DNA, and, if so, can museum specimens be used to reconstruct the historical epidemiology of malaria? In this Perspective, we explore molecular techniques practical to pathogen prospecting, which, more broadly, we define as the science of screening entomological museum specimens for human, animal, or plant pathogens. Historical DNA and pathogen prospecting provide a means of describing the coevolution of human, vector, and parasite, informing the development of insecticides, diagnostics, therapeutics, and vaccines.
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Affiliation(s)
- Mark P. Nelder
- Enteric, Zoonotic and Vector-Borne Diseases, Health Protection, Public Health Ontario, Toronto, ONM5G 1M1, Canada
| | - Rachel Schats
- Laboratory for Human Osteoarchaeology, Faculty of Archaeology, Leiden University, 2333 CCLeiden, The Netherlands
| | - Hendrik N. Poinar
- Department of Anthropology, McMaster University, Hamilton, ONL8S 4L9, Canada
- Department of Biochemistry, McMaster University, Hamilton, ONL8S 4L9, Canada
- McMaster Ancient DNA Centre, Department of Anthropology, McMaster University, Hamilton, ONL8S 4L9, Canada
| | - Amanda Cooke
- Department of Anthropology, McMaster University, Hamilton, ONL8S 4L9, Canada
| | - Megan B. Brickley
- Department of Anthropology, McMaster University, Hamilton, ONL8S 4L9, Canada
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Jiang A, Lee M, Selvaraj P, Degefa T, Getachew H, Merga H, Yewhalaw D, Yan G, Hsu K. Investigating the Impact of Irrigation on Malaria Vector Larval Habitats and Transmission Using a Hydrology-Based Model. GEOHEALTH 2023; 7:e2023GH000868. [PMID: 38089068 PMCID: PMC10711417 DOI: 10.1029/2023gh000868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 10/13/2023] [Accepted: 11/20/2023] [Indexed: 02/01/2024]
Abstract
A combination of accelerated population growth and severe droughts has created pressure on food security and driven the development of irrigation schemes across sub-Saharan Africa. Irrigation has been associated with increased malaria risk, but risk prediction remains difficult due to the heterogeneity of irrigation and the environment. While investigating transmission dynamics is helpful, malaria models cannot be applied directly in irrigated regions as they typically rely only on rainfall as a source of water to quantify larval habitats. By coupling a hydrologic model with an agent-based malaria model for a sugarcane plantation site in Arjo, Ethiopia, we demonstrated how incorporating hydrologic processes to estimate larval habitats can affect malaria transmission. Using the coupled model, we then examined the impact of an existing irrigation scheme on malaria transmission dynamics. The inclusion of hydrologic processes increased the variability of larval habitat area by around two-fold and resulted in reduction in malaria transmission by 60%. In addition, irrigation increased all habitat types in the dry season by up to 7.4 times. It converted temporary and semi-permanent habitats to permanent habitats during the rainy season, which grew by about 24%. Consequently, malaria transmission was sustained all-year round and intensified during the main transmission season, with the peak shifted forward by around 1 month. Lastly, we evaluated the spatiotemporal distribution of adult vectors under the effect of irrigation by resolving habitat heterogeneity. These findings could help larval source management by identifying transmission hotspots and prioritizing resources for malaria elimination planning.
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Affiliation(s)
- Ai‐Ling Jiang
- Department of Civil and Environmental EngineeringCenter for Hydrometeorology and Remote SensingUniversity of California IrvineIrvineCAUSA
| | - Ming‐Chieh Lee
- Department of Population Health and Disease PreventionSchool of Public HealthSusan and Henry Samueli College of Health SciencesUniversity of California IrvineIrvineCAUSA
| | - Prashanth Selvaraj
- Institute for Disease ModelingBill and Melinda Gates FoundationSeattleWAUSA
| | - Teshome Degefa
- School of Medical Laboratory SciencesInstitute of HealthJimma UniversityJimmaEthiopia
- Tropical and Infectious Diseases Research Center (TIDRC)Jimma UniversityJimmaEthiopia
| | - Hallelujah Getachew
- School of Medical Laboratory SciencesInstitute of HealthJimma UniversityJimmaEthiopia
- Tropical and Infectious Diseases Research Center (TIDRC)Jimma UniversityJimmaEthiopia
- Department of Medical Laboratory TechnologyArbaminch College of Health SciencesArba MinchEthiopia
| | - Hailu Merga
- Department of EpidemiologyInstitute of HealthJimma UniversityJimmaEthiopia
| | - Delenasaw Yewhalaw
- School of Medical Laboratory SciencesInstitute of HealthJimma UniversityJimmaEthiopia
- Tropical and Infectious Diseases Research Center (TIDRC)Jimma UniversityJimmaEthiopia
| | - Guiyun Yan
- Department of Population Health and Disease PreventionSchool of Public HealthSusan and Henry Samueli College of Health SciencesUniversity of California IrvineIrvineCAUSA
| | - Kuolin Hsu
- Department of Civil and Environmental EngineeringCenter for Hydrometeorology and Remote SensingUniversity of California IrvineIrvineCAUSA
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4
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Leal Filho W, May J, May M, Nagy GJ. Climate change and malaria: some recent trends of malaria incidence rates and average annual temperature in selected sub-Saharan African countries from 2000 to 2018. Malar J 2023; 22:248. [PMID: 37641080 PMCID: PMC10464074 DOI: 10.1186/s12936-023-04682-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Malaria is still a disease of massive burden in Africa, also influenced by climate change. The fluctuations and trends of the temperature and precipitation are well-known determinant factors influencing the disease's vectors and incidence rates. This study provides a concise account of malaria trends. It describes the association between average temperature and malaria incidence rates (IR) in nine sub-Saharan African countries: Nigeria, Ethiopia, South Africa, Kenya, Uganda, Ghana, Mozambique, Zambia and Zimbabwe. The incidence of malaria can vary both in areas where the disease is already present, and in regions where it is present in low numbers or absent. The increased vulnerability to the disease under increasing average temperatures and humidity is due to the new optimal level for vector breeding in areas where vector populations and transmission are low, and populations are sensitive due to low acquired immunity. METHODS A second source trend analysis was carried out of malaria cases and incidence rates (the number of new malaria cases per 1000 population at risk per year) with data from the World Health Organization (WHO) and average annual mean temperature from 2000 to 2018 from the World Bank's Climate Change Knowledge Portal (CCKP). Additionally, descriptive epidemiological methods were used to describe the development and trends in the selected countries. Furthermore, MS Excel was chosen for data analysis and visualization. RESULTS Findings obtained from this article align with the recent literature, highlighting a declining trend (20-80%) of malaria IR (incidence rate) from 2000 to 2018. However, malaria IR varies considerably, with high values in Uganda, Mozambique, Nigeria and Zambia, moderate values in Ghana, Zimbabwe, and Kenya, and low values in South Africa and Ethiopia in 2018. Evidence suggests varying IRs after average temperature fluctuations in several countries (e.g., Zimbabwe, Ethiopia). Also, an inverse temperature-IR relationship occurs, the sharp decrease of IR during 2012-2014 and 2000-2003, respectively, occurred with increasing average temperatures in Ghana and Nigeria. The decreasing trends and fluctuations, partly accompanying the temperature, should result from the intervention programmes and rainfall variability. The vulnerability and changing climate could arrest the recent trends of falling IR. CONCLUSION Thus, malaria is still a crucial public health issue in sub-Saharan Africa, although a robust decreasing IR occurred in most studied countries.
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Affiliation(s)
- Walter Leal Filho
- Research and Transfer Centre "Sustainable Development and Climate Change Management", Hamburg University of Applied Sciences, Ulmenliet 20, 21033, Hamburg, Germany
- Department of Natural Sciences, Manchester Metropolitan University, Manchester, M15 6BH, UK
| | - Julia May
- Research and Transfer Centre "Sustainable Development and Climate Change Management", Hamburg University of Applied Sciences, Ulmenliet 20, 21033, Hamburg, Germany.
| | - Marta May
- Research and Transfer Centre "Sustainable Development and Climate Change Management", Hamburg University of Applied Sciences, Ulmenliet 20, 21033, Hamburg, Germany
| | - Gustavo J Nagy
- Instituto de Ecología y Ciencias Ambientales, Facultad de Ciencias, Universidad de la República, UdelaR, Montevideo, Uruguay
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Yaladanda N, Mopuri R, Vavilala H, Bhimala KR, Gouda KC, Kadiri MR, Upadhyayula SM, Mutheneni SR. The synergistic effect of climatic factors on malaria transmission: a predictive approach for northeastern states of India. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:59194-59211. [PMID: 36997790 DOI: 10.1007/s11356-023-26672-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 03/23/2023] [Indexed: 05/10/2023]
Abstract
The northeast region of India is highlighted as the most vulnerable region for malaria. This study attempts to explore the epidemiological profile and quantify the climate-induced influence on malaria cases in the context of tropical states, taking Meghalaya and Tripura as study areas. Monthly malaria cases and meteorological data from 2011 to 2018 and 2013 to 2019 were collected from the states of Meghalaya and Tripura, respectively. The nonlinear associations between individual and synergistic effect of meteorological factors and malaria cases were assessed, and climate-based malaria prediction models were developed using the generalized additive model (GAM) with Gaussian distribution. During the study period, a total of 216,943 and 125,926 cases were recorded in Meghalaya and Tripura, respectively, and majority of the cases occurred due to the infection of Plasmodium falciparum in both the states. The temperature and relative humidity in Meghalaya and temperature, rainfall, relative humidity, and soil moisture in Tripura showed a significant nonlinear effect on malaria; moreover, the synergistic effects of temperature and relative humidity (SI=2.37, RERI=0.58, AP=0.29) and temperature and rainfall (SI=6.09, RERI=2.25, AP=0.61) were found to be the key determinants of malaria transmission in Meghalaya and Tripura, respectively. The developed climate-based malaria prediction models are able to predict the malaria cases accurately in both Meghalaya (RMSE: 0.0889; R2: 0.944) and Tripura (RMSE: 0.0451; R2: 0.884). The study found that not only the individual climatic factors can significantly increase the risk of malaria transmission but also the synergistic effects of climatic factors can drive the malaria transmission multifold. This reminds the policymakers to pay attention to the control of malaria in situations with high temperature and relative humidity and high temperature and rainfall in Meghalaya and Tripura, respectively.
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Affiliation(s)
- Nikhila Yaladanda
- EIACP Resource Partner on Climate Change and Public Health, Applied Biology Division, CSIR-Indian Institute of Chemical Technology (CSIR-IICT), Tarnaka, Hyderabad, Telangana, 500007, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Rajasekhar Mopuri
- EIACP Resource Partner on Climate Change and Public Health, Applied Biology Division, CSIR-Indian Institute of Chemical Technology (CSIR-IICT), Tarnaka, Hyderabad, Telangana, 500007, India
| | - Hariprasad Vavilala
- EIACP Resource Partner on Climate Change and Public Health, Applied Biology Division, CSIR-Indian Institute of Chemical Technology (CSIR-IICT), Tarnaka, Hyderabad, Telangana, 500007, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Kantha Rao Bhimala
- CSIR-Fourth Paradigm Institute, NAL Belur Campus, Bangalore, Karnataka, 560037, India
| | - Krushna Chandra Gouda
- CSIR-Fourth Paradigm Institute, NAL Belur Campus, Bangalore, Karnataka, 560037, India
| | - Madhusudhan Rao Kadiri
- EIACP Resource Partner on Climate Change and Public Health, Applied Biology Division, CSIR-Indian Institute of Chemical Technology (CSIR-IICT), Tarnaka, Hyderabad, Telangana, 500007, India
| | - Suryanarayana Murty Upadhyayula
- National Institute of Pharmaceutical Education and Research (NIPER), Sila Katamur, Halugurisuk, Changsari, Kamrup, Assam, 781101, India
| | - Srinivasa Rao Mutheneni
- EIACP Resource Partner on Climate Change and Public Health, Applied Biology Division, CSIR-Indian Institute of Chemical Technology (CSIR-IICT), Tarnaka, Hyderabad, Telangana, 500007, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
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Baharom M, Soffian SSS, Peng CS, Baharudin MH, Mirza U, Madrim MF, Jeffree MS, Rahim SSSA, Hassan MR. Projecting Malaria Incidence Based on Climate Change Modeling Approach: A Systematic Review. Open Access Maced J Med Sci 2022. [DOI: 10.3889/oamjms.2022.10141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND: Climate change will affect the transmission of malaria by shifting the geographical space of the vector.
AIM: The review aims to examine the climate change modeling approach and climatic variables used for malaria projection.
METHODS: Articles were systematically searched from four databases, Scopus, Web of Science, PubMed, and SAGE. The PICO concept was used for formulation search and PRISMA approach to identify the final articles.
RESULTS: A total of 27 articles were retrieved and reviewed. There were six climate factors identified in this review: Temperature, rainfall/precipitation, humidity, wind, solar radiation, and climate change scenarios. Modeling approaches used to project future malarial trend includes mathematical and computational approach.
CONCLUSION: This review provides robust evidence of an association between the impact of climate change and malaria incidence. Prediction on seasonal patterns would be useful for malaria surveillance in public health prevention and mitigation strategies.
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7
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Relationships between transmission of malaria in Africa and climate factors. Sci Rep 2022; 12:14392. [PMID: 35999450 PMCID: PMC9399114 DOI: 10.1038/s41598-022-18782-9] [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: 02/14/2022] [Accepted: 08/18/2022] [Indexed: 11/09/2022] Open
Abstract
The spread of malaria is related to climate change because temperature and rainfall are key parameters of climate change. Fluctuations in temperature affect the spread of malaria by lowering or speeding up its rate of transmission. The amount of rainfall also affects the transmission of malaria by offering a lot of sites suitable for mosquitoes to breed in. However, a high amount of rainfall does not have a great effect. Because of the high malaria incidence and the death rates in African regions, by using malaria incidence data, temperature data and rainfall data collected in 1901-2015, we construct and analyze climate networks to show how climate relates to the transmission of malaria in African countries. Malaria networks show a positive correlation with temperature and rainfall networks, except for the 1981-2015 period, in which the malaria network shows a negative correlation with rainfall.
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8
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Nili S, Asadgol Z, Dalaei H, Khanjani N, Bakhtiari B, Jahani Y. The effect of climate change on malaria transmission in the southeast of Iran. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2022; 66:1613-1626. [PMID: 35713696 DOI: 10.1007/s00484-022-02305-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 05/08/2022] [Accepted: 05/11/2022] [Indexed: 06/15/2023]
Abstract
Malaria is a vector-borne disease, likely to be affected by climate change. In this study, general circulation model (GCM)-based scenarios were used for projecting future climate patterns and malaria incidence by artificial neural networks (ANN) in Zahedan district, Iran. Daily malaria incidence data of Zahedan district from 2000 to 2019 were inquired. The gamma test was used to select the appropriate combination of parameters for nonlinear modeling. The future climate pattern projections were obtained from HadGEM2-ES. The output was downscaled using LARS-WG stochastic weather generator under two Representative Concentration Pathway (RCP2.6 and RCP8.5) scenarios. The effect of climate change on malaria transmission for 2021-2060 was simulated by ANN. The designed model indicated that the future climate in Zahedan district will be warmer, more humid, and with more precipitation. Assessment of the potential impact of climate change on the incidence of malaria by ANN showed the number of malaria cases in Zahedan under both scenarios (RCP2.6 and RCP 8.5). It should be noted that due to the lack of daily malaria data before 2013, monthly data from 2000 were used only for initial analysis; and in preprocessing and simulation analyses, the daily malaria data from 2013 to 2019 were used. Therefore, if proper interventions are not implemented, malaria will continue to be a health issue in this region.
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Affiliation(s)
- Sairan Nili
- Faculty of Public Health, Kurdistan University of Medical Sciences, Sanandaj, Iran
- Social Determinants of Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | | | - Hamideh Dalaei
- Research Deputy of Iranian Meteorological Organization (IRIMO), Tehran, Iran
| | - Narges Khanjani
- Environmental Health Engineering Research Center, Kerman University of Medical Sciences, Kerman, Iran.
- Monash Centre for Occupational & Environmental Health, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
- Department of Epidemiology and Biostatistics, School of Public Health, Kerman University of Medical Sciences, Kerman, Iran.
| | - Bahram Bakhtiari
- Water Engineering Department, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Younes Jahani
- Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
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Semenza JC, Rocklöv J, Ebi KL. Climate Change and Cascading Risks from Infectious Disease. Infect Dis Ther 2022; 11:1371-1390. [PMID: 35585385 PMCID: PMC9334478 DOI: 10.1007/s40121-022-00647-3] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
Climate change is adversely affecting the burden of infectious disease throughout the world, which is a health security threat. Climate-sensitive infectious disease includes vector-borne diseases such as malaria, whose transmission potential is expected to increase because of enhanced climatic suitability for the mosquito vector in Asia, sub-Saharan Africa, and South America. Climatic suitability for the mosquitoes that can carry dengue, Zika, and chikungunya is also likely to increase, facilitating further increases in the geographic range and longer transmission seasons, and raising concern for expansion of these diseases into temperate zones, particularly under higher greenhouse gas emission scenarios. Early spring temperatures in 2018 seem to have contributed to the early onset and extensive West Nile virus outbreak in Europe, a pathogen expected to expand further beyond its current distribution, due to a warming climate. As for tick-borne diseases, climate change is projected to continue to contribute to the spread of Lyme disease and tick-borne encephalitis, particularly in North America and Europe. Schistosomiasis is a water-borne disease and public health concern in Africa, Latin America, the Middle East, and Southeast Asia; climate change is anticipated to change its distribution, with both expansions and contractions expected. Other water-borne diseases that cause diarrheal diseases have declined significantly over the last decades owing to socioeconomic development and public health measures but changes in climate can reverse some of these positive developments. Weather and climate events, population movement, land use changes, urbanization, global trade, and other drivers can catalyze a succession of secondary events that can lead to a range of health impacts, including infectious disease outbreaks. These cascading risk pathways of causally connected events can result in large-scale outbreaks and affect society at large. We review climatic and other cascading drivers of infectious disease with projections under different climate change scenarios. Supplementary file1 (MP4 328467 KB).
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Affiliation(s)
- Jan C Semenza
- Heidelberg Institute of Global Health, University of Heidelberg, 69120, Heidelberg, Germany.
| | - Joacim Rocklöv
- Section of Sustainable Health, Department of Public Health and Clinical Medicine, Umeå University, 901 87, Umeå, Sweden
- Heidelberg Institute of Global Health (HIGH), Interdisciplinary Centre for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 205, 69120, Heidelberg, Germany
| | - Kristie L Ebi
- Center for Health and the Global Environment (CHanGE), University of Washington, Seattle, WA, 98195, USA
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Okiring J, Routledge I, Epstein A, Namuganga JF, Kamya EV, Obeng-Amoako GO, Sebuguzi CM, Rutazaana D, Kalyango JN, Kamya MR, Dorsey G, Wesonga R, Kiwuwa SM, Nankabirwa JI. Associations between environmental covariates and temporal changes in malaria incidence in high transmission settings of Uganda: a distributed lag nonlinear analysis. BMC Public Health 2021; 21:1962. [PMID: 34717583 PMCID: PMC8557030 DOI: 10.1186/s12889-021-11949-5] [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: 05/02/2021] [Accepted: 10/08/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Environmental factors such as temperature, rainfall, and vegetation cover play a critical role in malaria transmission. However, quantifying the relationships between environmental factors and measures of disease burden relevant for public health can be complex as effects are often non-linear and subject to temporal lags between when changes in environmental factors lead to changes in malaria incidence. The study investigated the effect of environmental covariates on malaria incidence in high transmission settings of Uganda. METHODS This study leveraged data from seven malaria reference centres (MRCs) located in high transmission settings of Uganda over a 24-month period. Estimates of monthly malaria incidence (MI) were derived from MRCs' catchment areas. Environmental data including monthly temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed lag nonlinear model was used to investigate the effect of environmental covariates on malaria incidence. RESULTS Overall, the median (range) monthly temperature was 30 °C (26-47), rainfall 133.0 mm (3.0-247), NDVI 0.66 (0.24-0.80) and MI was 790 per 1000 person-years (73-3973). Temperature of 35 °C was significantly associated with malaria incidence compared to the median observed temperature (30 °C) at month lag 2 (IRR: 2.00, 95% CI: 1.42-2.83) and the increased cumulative IRR of malaria at month lags 1-4, with the highest cumulative IRR of 8.16 (95% CI: 3.41-20.26) at lag-month 4. Rainfall of 200 mm significantly increased IRR of malaria compared to the median observed rainfall (133 mm) at lag-month 0 (IRR: 1.24, 95% CI: 1.01-1.52) and the increased cumulative IRR of malaria at month lags 1-4, with the highest cumulative IRR of 1.99(95% CI: 1.22-2.27) at lag-month 4. Average NVDI of 0.72 significantly increased the cumulative IRR of malaria compared to the median observed NDVI (0.66) at month lags 2-4, with the highest cumulative IRR of 1.57(95% CI: 1.09-2.25) at lag-month 4. CONCLUSIONS In high-malaria transmission settings, high values of environmental covariates were associated with increased cumulative IRR of malaria, with IRR peaks at variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.
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Affiliation(s)
- Jaffer Okiring
- Clinical Epidemiology Unit, School of Medicine, Makerere University College of Health Sciences, Kampala, Uganda.
- Infectious Diseases Research Collaboration, 2C Nakasero Hill Road, Kampala, Uganda.
| | - Isobel Routledge
- Department of Epidemiology and Biostatistics, University of California, San Francisco, USA
| | - Adrienne Epstein
- Department of Epidemiology and Biostatistics, University of California, San Francisco, USA
| | - Jane F Namuganga
- Infectious Diseases Research Collaboration, 2C Nakasero Hill Road, Kampala, Uganda
| | - Emmanuel V Kamya
- Infectious Diseases Research Collaboration, 2C Nakasero Hill Road, Kampala, Uganda
| | - Gloria Odei Obeng-Amoako
- Clinical Epidemiology Unit, School of Medicine, Makerere University College of Health Sciences, Kampala, Uganda
| | | | - Damian Rutazaana
- National Malaria Control Division, Ministry of Health, Kampala, Uganda
| | - Joan N Kalyango
- Clinical Epidemiology Unit, School of Medicine, Makerere University College of Health Sciences, Kampala, Uganda
| | - Moses R Kamya
- Infectious Diseases Research Collaboration, 2C Nakasero Hill Road, Kampala, Uganda
- School of Medicine, Makerere University College of Health Sciences, Kampala, Uganda
| | - Grant Dorsey
- Department of Medicine, University of California, San Francisco, USA
| | - Ronald Wesonga
- Department of Statistics, College of Science, Sultan Qaboos University, Muscat, Oman
| | - Steven M Kiwuwa
- Department of Child Health and Development Centre, School of Medicine, Makerere University College of Health Sciences, Kampala, Uganda
| | - Joaniter I Nankabirwa
- Clinical Epidemiology Unit, School of Medicine, Makerere University College of Health Sciences, Kampala, Uganda
- Infectious Diseases Research Collaboration, 2C Nakasero Hill Road, Kampala, Uganda
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11
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Carrasco-Escobar G, Qquellon J, Villa D, Cava R, Llanos-Cuentas A, Benmarhnia T. Time-Varying Effects of Meteorological Variables on Malaria Epidemiology in the Context of Interrupted Control Efforts in the Amazon Rainforest, 2000-2017. Front Med (Lausanne) 2021; 8:721515. [PMID: 34660633 PMCID: PMC8511324 DOI: 10.3389/fmed.2021.721515] [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: 06/07/2021] [Accepted: 08/27/2021] [Indexed: 11/25/2022] Open
Abstract
Successful malaria control interventions, mostly based on the training of health workers, distribution of insecticide-treated nets, and spraying, decrease malaria incidence; however, when these interventions are interrupted, a resurgence may occur. In the Peruvian Amazon, after discontinuing the control activities implemented by the PAMAFRO project (2006–2010)-a Global Fund-sponsored project for the strengthening of malaria control and surveillance in multiple countries in Latin America– malaria cases re-emerged dramatically. In parallel, meteorological factors determine the conditions suitable for the development, reproduction, and survival of mosquito vectors and parasites. This study hypothesized that interruption of malaria interventions may have modified the meteorological-malaria relationships over time (i.e., temporal changes in the dose-response between meteorological variables and malaria incidence). In this panel data analysis, we assessed the extent that relationships between meteorological variables and malaria changed temporally using data of monthly malaria incidence due to Plasmodium vivax or P. falciparum in Loreto, Peru (2000–2017). Generalized additive models were used to explore how the effects of meteorological variables changed in magnitude before, during, and after the PAMAFRO intervention. We found that once the PAMAFRO intervention had been interrupted, the estimated effects (dose-response) of meteorological variables on incidence rates decreased for both malaria parasite species. However, these fitted effect estimates did not reach their baseline levels (before the PAMAFRO period); variations of time-varying slopes between 0.45 and 2.07 times were observed after the PAMAFRO intervention. We also reported significant heterogeneity in the geographical distributions of malaria, parasite species, and meteorological variables. High malaria transmission occurred consistently in the northwestern provinces of Loreto Department. Since the end of the PAMAFRO period, a higher effect of precipitation and actual evapotranspiration was described on P. falciparum compared to P. vivax. The effect of temperature on malaria was greater over a shorter time (1-month lag or less), compared with precipitation and actual evapotranspiration (12-month lag). These findings demonstrate the importance of sustained malaria control efforts since interruption may enhance the links between meteorological factors and malaria. Our results also emphasize the importance of considering the time-varying effect of meteorological factors on malaria incidence to tailor control interventions, especially to better manage the current and future climate change crisis.
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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.,Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, United States
| | - Jazmin Qquellon
- Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Diego Villa
- Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Renato Cava
- Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Alejandro Llanos-Cuentas
- Facultad de Salud Pública y Administración, Universidad Peruana Cayetano Heredia, Lima, Peru.,Instituto de Medicina Tropical "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Tarik Benmarhnia
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, United States.,Scripps Institution of Oceanography, University of California, San Diego, San Diego, CA, United States
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Krainara P, Dumrongrojwatthana P, Bhattarakosol P. Significant factors associated with malaria spread in Thailand: a cross-sectional study. JOURNAL OF HEALTH RESEARCH 2021. [DOI: 10.1108/jhr-11-2020-0575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Purpose
This paper aims to uncover new factors that influence the spread of malaria.
Design/methodology/approach
The historical data related to malaria were collected from government agencies. Later, the data were cleaned and standardized before passing through the analysis process. To obtain the simplicity of these numerous factors, the first procedure involved in executing the factor analysis where factors' groups related to malaria distribution were determined. Therefore, machine learning was deployed, and the confusion matrices are computed. The results from machine learning techniques were further analyzed with logistic regression to study the relationship of variables affecting malaria distribution.
Findings
This research can detect 28 new noteworthy factors. With all the defined factors, the logistics model tree was constructed. The precision and recall of this tree are 78% and 82.1%, respectively. However, when considering the significance of all 28 factors under the logistic regression technique using forward stepwise, the indispensable factors have been found as the number of houses without electricity (houses), number of irrigation canals (canals), number of shallow wells (places) and number of migrated persons (persons). However, all 28 factors must be included to obtain high accuracy in the logistics model tree.
Originality/value
This paper may lead to highly-efficient government development plans, including proper financial management for malaria control sections. Consequently, the spread of malaria can be reduced naturally.
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Ateba FF, Sagara I, Sogoba N, Touré M, Konaté D, Diawara SI, Diakité SAS, Diarra A, Coulibaly MD, Dolo M, Dolo A, Sacko A, Thiam SM, Sissako A, Sangaré L, Diakité M, Koita OA, Cissoko M, Traore SF, Winch PJ, Febrero-Bande M, Shaffer JG, Krogtad DJ, Marker HC, Doumbia S, Gaudart J. Spatio-Temporal Dynamic of Malaria Incidence: A Comparison of Two Ecological Zones in Mali. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E4698. [PMID: 32629876 PMCID: PMC7370019 DOI: 10.3390/ijerph17134698] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 06/19/2020] [Accepted: 06/23/2020] [Indexed: 02/06/2023]
Abstract
Malaria transmission largely depends on environmental, climatic, and hydrological conditions. In Mali, malaria epidemiological patterns are nested within three ecological zones. This study aimed at assessing the relationship between those conditions and the incidence of malaria in Dangassa and Koila, Mali. Malaria data was collected through passive case detection at community health facilities of each study site from June 2015 to January 2017. Climate and environmental data were obtained over the same time period from the Goddard Earth Sciences (Giovanni) platform and hydrological data from Mali hydraulic services. A generalized additive model was used to determine the lagged time between each principal component analysis derived component and the incidence of malaria cases, and also used to analyze the relationship between malaria and the lagged components in a multivariate approach. Malaria transmission patterns were bimodal at both sites, but peak and lull periods were longer lasting for Koila study site. Temperatures were associated with malaria incidence in both sites. In Dangassa, the wind speed (p = 0.005) and river heights (p = 0.010) contributed to increasing malaria incidence, in contrast to Koila, where it was humidity (p < 0.001) and vegetation (p = 0.004). The relationships between environmental factors and malaria incidence differed between the two settings, implying different malaria dynamics and adjustments in the conception and plan of interventions.
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Affiliation(s)
- François Freddy Ateba
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.K.); (S.I.D.); (S.A.S.D.); (A.D.); (M.D.C.); (M.D.); (A.D.); (S.M.T.); (M.D.); (M.C.); (S.F.T.)
- Department of Mathematics, University of Quebec at Montreal (UQAM), Montréal, QC H2X 3Y7, Canada
| | - Issaka Sagara
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.K.); (S.I.D.); (S.A.S.D.); (A.D.); (M.D.C.); (M.D.); (A.D.); (S.M.T.); (M.D.); (M.C.); (S.F.T.)
- Department of Public Health Education and Research, Faculty of Medicine and Odonto-Stomatology, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali;
| | - Nafomon Sogoba
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.K.); (S.I.D.); (S.A.S.D.); (A.D.); (M.D.C.); (M.D.); (A.D.); (S.M.T.); (M.D.); (M.C.); (S.F.T.)
| | - Mahamoudou Touré
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.K.); (S.I.D.); (S.A.S.D.); (A.D.); (M.D.C.); (M.D.); (A.D.); (S.M.T.); (M.D.); (M.C.); (S.F.T.)
| | - Drissa Konaté
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.K.); (S.I.D.); (S.A.S.D.); (A.D.); (M.D.C.); (M.D.); (A.D.); (S.M.T.); (M.D.); (M.C.); (S.F.T.)
| | - Sory Ibrahim Diawara
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.K.); (S.I.D.); (S.A.S.D.); (A.D.); (M.D.C.); (M.D.); (A.D.); (S.M.T.); (M.D.); (M.C.); (S.F.T.)
| | - Séidina Aboubacar Samba Diakité
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.K.); (S.I.D.); (S.A.S.D.); (A.D.); (M.D.C.); (M.D.); (A.D.); (S.M.T.); (M.D.); (M.C.); (S.F.T.)
| | - Ayouba Diarra
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.K.); (S.I.D.); (S.A.S.D.); (A.D.); (M.D.C.); (M.D.); (A.D.); (S.M.T.); (M.D.); (M.C.); (S.F.T.)
| | - Mamadou D. Coulibaly
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.K.); (S.I.D.); (S.A.S.D.); (A.D.); (M.D.C.); (M.D.); (A.D.); (S.M.T.); (M.D.); (M.C.); (S.F.T.)
| | - Mathias Dolo
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.K.); (S.I.D.); (S.A.S.D.); (A.D.); (M.D.C.); (M.D.); (A.D.); (S.M.T.); (M.D.); (M.C.); (S.F.T.)
| | - Amagana Dolo
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.K.); (S.I.D.); (S.A.S.D.); (A.D.); (M.D.C.); (M.D.); (A.D.); (S.M.T.); (M.D.); (M.C.); (S.F.T.)
| | - Aissata Sacko
- Department of Public Health Education and Research, Faculty of Medicine and Odonto-Stomatology, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali;
| | - Sidibe M’baye Thiam
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.K.); (S.I.D.); (S.A.S.D.); (A.D.); (M.D.C.); (M.D.); (A.D.); (S.M.T.); (M.D.); (M.C.); (S.F.T.)
| | - Aliou Sissako
- Laboratory of Applied Molecular Biology (LBMA), Science and Technologies Faculty (FST), University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (A.S.); (L.S.); (O.A.K.)
| | - Lansana Sangaré
- Laboratory of Applied Molecular Biology (LBMA), Science and Technologies Faculty (FST), University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (A.S.); (L.S.); (O.A.K.)
| | - Mahamadou Diakité
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.K.); (S.I.D.); (S.A.S.D.); (A.D.); (M.D.C.); (M.D.); (A.D.); (S.M.T.); (M.D.); (M.C.); (S.F.T.)
| | - Ousmane A. Koita
- Laboratory of Applied Molecular Biology (LBMA), Science and Technologies Faculty (FST), University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (A.S.); (L.S.); (O.A.K.)
| | - Mady Cissoko
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.K.); (S.I.D.); (S.A.S.D.); (A.D.); (M.D.C.); (M.D.); (A.D.); (S.M.T.); (M.D.); (M.C.); (S.F.T.)
- APHM, INSERM, IRD, SESSTIM, Hop Timone, BioSTIC, Biostatistic & ICT, Aix Marseille Université, 13005 Marseille, France
| | - Sékou Fantamady Traore
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.K.); (S.I.D.); (S.A.S.D.); (A.D.); (M.D.C.); (M.D.); (A.D.); (S.M.T.); (M.D.); (M.C.); (S.F.T.)
| | - Peter John Winch
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA; (P.J.W.); (H.C.M.)
| | - Manuel Febrero-Bande
- Department of Statistics, Mathematical Analysis and Optimization, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain;
| | - Jeffrey G. Shaffer
- Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, United States of America, 1440 Canal Street New Orleans, LA 70112, USA; (J.G.S.); (D.J.K.)
| | - Donald J. Krogtad
- Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, United States of America, 1440 Canal Street New Orleans, LA 70112, USA; (J.G.S.); (D.J.K.)
| | - Hannah Catherine Marker
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA; (P.J.W.); (H.C.M.)
| | - Seydou Doumbia
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.K.); (S.I.D.); (S.A.S.D.); (A.D.); (M.D.C.); (M.D.); (A.D.); (S.M.T.); (M.D.); (M.C.); (S.F.T.)
- Department of Public Health Education and Research, Faculty of Medicine and Odonto-Stomatology, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali;
| | - Jean Gaudart
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.K.); (S.I.D.); (S.A.S.D.); (A.D.); (M.D.C.); (M.D.); (A.D.); (S.M.T.); (M.D.); (M.C.); (S.F.T.)
- APHM, INSERM, IRD, SESSTIM, Hop Timone, BioSTIC, Biostatistic & ICT, Aix Marseille Université, 13005 Marseille, France
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Chaturvedi S, Dwivedi S. Estimating the malaria transmission over the Indian subcontinent in a warming environment using a dynamical malaria model. JOURNAL OF WATER AND HEALTH 2020; 18:358-374. [PMID: 32589621 DOI: 10.2166/wh.2020.148] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Malaria is a major public health problem in India. The malaria transmission is sensitive to climatic parameters. The regional population-related factors also influence malaria transmission. To take into account temperature and rainfall variability and associated population-related effects (in a changing climate) on the malaria transmission over India, a regional dynamical malaria model, namely VECTRI (vector-borne disease community model) is used. The daily temperature and rainfall data derived from the historical (years 1961-2005) and representative concentration pathway (years 2006-2050) runs of the Coupled Model Intercomparison Project Phase 5 models have been used for the analysis. The model results of the historical run are compared with the observational data. The spatio-temporal changes (region-specific as well as seasonal changes) in the malaria transmission as a result of climate change are quantified over the India. The parameters related to the breeding cycle of malaria as well as those which estimate the malaria cases are analyzed in the global warming scenario.
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Affiliation(s)
- Shweta Chaturvedi
- K Banerjee Centre of Atmospheric and Ocean Studies and M N Saha Centre of Space Studies, University of Allahabad, Allahabad, Uttar Pradesh 211002, India E-mail:
| | - Suneet Dwivedi
- K Banerjee Centre of Atmospheric and Ocean Studies and M N Saha Centre of Space Studies, University of Allahabad, Allahabad, Uttar Pradesh 211002, India E-mail:
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Gopal S, Ma Y, Xin C, Pitts J, Were L. Characterizing the Spatial Determinants and Prevention of Malaria in Kenya. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E5078. [PMID: 31842408 PMCID: PMC6950158 DOI: 10.3390/ijerph16245078] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2019] [Revised: 11/26/2019] [Accepted: 12/05/2019] [Indexed: 01/19/2023]
Abstract
The United Nations' Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of utilizing local spatial variations to uncover the statistical relationships between malaria incidence rate and environmental and behavioral factors across the counties of Kenya. Two data sources are used-Kenya Demographic and Health Surveys of 2000, 2005, 2010, and 2015, and the national Malaria Indicator Survey of 2015. The spatial analysis shows clustering of counties with high malaria incidence rate, or hot spots, in the Lake Victoria region and the east coastal area around Mombasa; there are significant clusters of counties with low incidence rate, or cold spot areas in Nairobi. We apply an analysis technique, geographically weighted regression, that helps to better model how environmental and social determinants are related to malaria incidence rate while accounting for the confounding effects of spatial non-stationarity. Some general patterns persist over the four years of observation. We establish that variables including rainfall, proximity to water, vegetation, and population density, show differential impacts on the incidence of malaria in Kenya. The El-Nino-southern oscillation (ENSO) event in 2015 was significant in driving up malaria in the southern region of Lake Victoria compared with prior time-periods. The applied spatial multivariate clustering analysis indicates the significance of social and behavioral survey responses. This study can help build a better spatially explicit predictive model for malaria in Kenya capturing the role and spatial distribution of environmental, social, behavioral, and other characteristics of the households.
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Affiliation(s)
- Sucharita Gopal
- Department of Earth & Environment, Boston University, Boston, MA 02215, USA; (S.G.); (Y.M.); (C.X.)
- Center for Global Development Policy, Boston University, Boston, MA 02215, USA;
| | - Yaxiong Ma
- Department of Earth & Environment, Boston University, Boston, MA 02215, USA; (S.G.); (Y.M.); (C.X.)
| | - Chen Xin
- Department of Earth & Environment, Boston University, Boston, MA 02215, USA; (S.G.); (Y.M.); (C.X.)
| | - Joshua Pitts
- Center for Global Development Policy, Boston University, Boston, MA 02215, USA;
| | - Lawrence Were
- College of Health & Rehabilitation Sciences: Sargent College, Boston University, Boston, MA 02215, USA
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Cáceres Carrera L, Victoria C, Ramirez JL, Jackman C, Calzada JE, Torres R. Study of the epidemiological behavior of malaria in the Darien Region, Panama. 2015-2017. PLoS One 2019; 14:e0224508. [PMID: 31730618 PMCID: PMC6857920 DOI: 10.1371/journal.pone.0224508] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 10/15/2019] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Malaria is endemic in Darién and an assessment of the different factors affecting its epidemiology is crucial for the development of adequate strategies of surveillance, prevention, and disease control. The objective of this study was to determine the main characteristics of the epidemiological behavior of malaria in the Darien region. METHODS This research was comprised of a retrospective analysis to determine the incidence and malaria distribution in the Darien region from 2015 to 2017. We evaluated malaria indicators, disease distribution, incidence (by age group and sex), diagnostic methods, treatment, and control measures. In addition, we examined the cross-border migration activity and its possible contribution to the maintenance and distribution of malaria. RESULTS During the period of 2015-2017, we examined 41,141 thick blood smear samples, out of which 501 tested positive for malaria. Plasmodium vivax was responsible for 92.2% of those infections. Males comprised 62.7% of the total diagnosed cases. Meanwhile, a similar percentage, 62.7%, of the total cases were registered in economically active ages. The more frequent symptoms included fever (99.4%) and chills (97.4%), with 53.1% of cases registering between 2,000 and 6,000 parasites/μl of blood. The annual parasitic incidence (API) average was 3.0/1,000 inhabitants, while the slide positivity rate (SPR) was 1.2% and the annual blood examination rate (ABER) 22.5%. In Darién there is a constant internal and cross-border migration movement between Panama and Colombia. Malaria control measures consisted of the active and passive search of suspected cases and of the application of vector control measures. CONCLUSION This study provides an additional perspective on malaria epidemiology in Darién. Additional efforts are required to intensify malaria surveillance and to achieve an effective control, eventually moving closer to the objective of malaria elimination. At the same time, there is a need for more eco-epidemiological, entomological and migratory studies to determine how these factors contribute to the patterns of maintenance and dissemination of malaria.
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Affiliation(s)
- Lorenzo Cáceres Carrera
- Department of Medical Entomology, Gorgas Memorial Institute of Health Studies, Panama City, Panama
- * E-mail:
| | | | - Jose L. Ramirez
- Crop Bioprotection Research Unit, National Center for Agricultural Utilization Research, Agricultural Research Service, United States Department of Agriculture, Peoria, Illinois, United States of America
| | - Carmela Jackman
- Epidemiology Department of the Darién Region, Ministry of Health, Panama City, Panama
| | - José E. Calzada
- Direcction of Research and Technological Development, Gorgas Memorial Institute of Health Studies, Panama City, Panama
| | - Rolando Torres
- Department of Medical Entomology, Gorgas Memorial Institute of Health Studies, Panama City, Panama
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Le PVV, Kumar P, Ruiz MO. Stochastic lattice-based modelling of malaria dynamics. Malar J 2018; 17:250. [PMID: 29976221 PMCID: PMC6034346 DOI: 10.1186/s12936-018-2397-z] [Citation(s) in RCA: 8] [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: 01/25/2018] [Accepted: 06/22/2018] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND The transmission of malaria is highly variable and depends on a range of climatic and anthropogenic factors. In addition, the dispersal of Anopheles mosquitoes is a key determinant that affects the persistence and dynamics of malaria. Simple, lumped-population models of malaria prevalence have been insufficient for predicting the complex responses of malaria to environmental changes. METHODS AND RESULTS A stochastic lattice-based model that couples a mosquito dispersal and a susceptible-exposed-infected-recovered epidemics model was developed for predicting the dynamics of malaria in heterogeneous environments. The It[Formula: see text] approximation of stochastic integrals with respect to Brownian motion was used to derive a model of stochastic differential equations. The results show that stochastic equations that capture uncertainties in the life cycle of mosquitoes and interactions among vectors, parasites, and hosts provide a mechanism for the disruptions of malaria. Finally, model simulations for a case study in the rural area of Kilifi county, Kenya are presented. CONCLUSIONS A stochastic lattice-based integrated malaria model has been developed. The applicability of the model for capturing the climate-driven hydrologic factors and demographic variability on malaria transmission has been demonstrated.
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Affiliation(s)
- Phong V. V. Le
- Department of Civil and Environmental Engineering, University of Illinois, Urbana, IL 61801 USA
- Faculty of Hydrology, Meteorology and Oceanography, Hanoi University of Science, Vietnam National University, Hanoi, Vietnam
| | - Praveen Kumar
- Department of Civil and Environmental Engineering, University of Illinois, Urbana, IL 61801 USA
- Department of Atmospheric Sciences, University of Illinois, Urbana, IL 61801 USA
| | - Marilyn O. Ruiz
- Department of Pathobiology, University of Illinois, Urbana, IL 61802 USA
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