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Valiati NCM, Rice B, Villela DAM. Disentangling the seasonality effects of malaria transmission in the Brazilian Amazon basin. ROYAL SOCIETY OPEN SCIENCE 2024; 11:231764. [PMID: 39076372 PMCID: PMC11285569 DOI: 10.1098/rsos.231764] [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: 11/09/2022] [Revised: 03/11/2024] [Accepted: 03/13/2024] [Indexed: 07/31/2024]
Abstract
The evidence of seasonal patterns in malaria epidemiology in the Brazilian Amazon basin indicates the need for a thorough investigation of seasonality in this last and heterogeneous region. Additionally, since these patterns are linked to climate variables, malaria models should also incorporate them. This study applies wavelet analysis to incidence data from 2003 to 2020 in the Epidemiological Surveillance System for Malaria (SIVEP-Malaria) database. A mathematical model with climate-dependent parametrization is proposed to study counts of malaria cases over time based on notification data, temperature and rainfall. The wavelet analysis reveals marked seasonality in states Amazonas and Amapá throughout the study period, and from 2003 to 2012 in Pará. However, these patterns are not as marked in other states such as Acre and Pará in more recent years. The wavelet coherency analysis indicates a strong association between incidence and temperature, especially for the municipalities of Macapá and Manaus, and a similar association for rainfall. The mathematical model fits well with the observed temporal trends in both municipalities. Studies on climate-dependent mathematical models provide a good assessment of the baseline epidemiology of malaria. Additionally, the understanding of seasonality effects and the application of models have great potential as tools for studying interventions for malaria control.
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Affiliation(s)
- Naiara C. M. Valiati
- National School of Public Health Sergio Arouca, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Benjamin Rice
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Daniel A. M. Villela
- Program of Scientific Computing, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
- Center for Health and Wellbeing, School of Public and International Affairs, Princeton University, Princeton, NJ, USA
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2
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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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3
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Oyegoke OO, Adewumi TS, Aderoju SA, Tsundzukani N, Mabunda E, Adeleke MA, Maharaj R, Okpeku M. Towards malaria elimination: analysis of travel history and case forecasting using the SARIMA model in Limpopo Province. Parasitol Res 2023:10.1007/s00436-023-07870-y. [PMID: 37310511 DOI: 10.1007/s00436-023-07870-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 05/08/2023] [Indexed: 06/14/2023]
Abstract
Despite various efforts and policy implementation aimed at controlling and eliminating malaria, imported malaria remains a major factor posing challenges in places that have made progress in malaria elimination. The persistence of malaria in Limpopo Province has largely been attributed to imported cases, thus reducing the pace of achieving the malaria-free target by 2025. Data from the Limpopo Malaria Surveillance Database System (2010-2020) was analyzed, and a seasonal auto-regressive integrated moving average (SARIMA) model was developed to forecast malaria incidence based on the incidence data's temporal autocorrelation. The study found that out of 57,288 people that were tested, 51,819 (90.5%) cases were local while 5469 (9.5%) cases were imported. Mozambique (44.9%), Zimbabwe (35.7%), and Ethiopia (8.5%) were the highest contributors of imported cases. The month of January recorded the highest incidence of cases while the least was in August. Analysis of the yearly figures showed an increasing trend and seasonal variation of recorded malaria cases. The SARIMA (3,1,1) X (3,1,0) [12] model used in predicting expected malaria case incidences for three consecutive years showed a decline in malaria incidences. The study demonstrated that imported malaria accounted for 9.5% of all cases. There is a need to re-focus on health education campaigns on malaria prevention methods and strengthening of indoor residual spray programs. Bodies collaborating toward malaria elimination in the Southern Africa region need to ensure a practical delivery of the objectives.
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Affiliation(s)
- Olukunle O Oyegoke
- Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Taiye S Adewumi
- Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Samuel A Aderoju
- Department of Mathematics and Statistics, Kwara State University, Ilorin, Nigeria
| | | | - Eric Mabunda
- Limpopo Department of Health, Malaria Control Program, Limpopo, South Africa
| | - Matthew A Adeleke
- Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Rajendra Maharaj
- Malaria Research Unit, South African Medical Research Council, Durban, South Africa
| | - Moses Okpeku
- Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa.
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4
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Fall P, Diouf I, Deme A, Diouf S, Sene D, Sultan B, Famien AM, Janicot S. Bias-Corrected CMIP5 Projections for Climate Change and Assessments of Impact on Malaria in Senegal under the VECTRI Model. Trop Med Infect Dis 2023; 8:310. [PMID: 37368728 DOI: 10.3390/tropicalmed8060310] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 05/19/2023] [Accepted: 05/29/2023] [Indexed: 06/29/2023] Open
Abstract
On the climate-health issue, studies have already attempted to understand the influence of climate change on the transmission of malaria. Extreme weather events such as floods, droughts, or heat waves can alter the course and distribution of malaria. This study aims to understand the impact of future climate change on malaria transmission using, for the first time in Senegal, the ICTP's community-based vector-borne disease model, TRIeste (VECTRI). This biological model is a dynamic mathematical model for the study of malaria transmission that considers the impact of climate and population variability. A new approach for VECTRI input parameters was also used. A bias correction technique, the cumulative distribution function transform (CDF-t) method, was applied to climate simulations to remove systematic biases in the Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) that could alter impact predictions. Beforehand, we use reference data for validation such as CPC global unified gauge-based analysis of daily precipitation (CPC for Climate Prediction Center), ERA5-land reanalysis, Climate Hazards InfraRed Precipitation with Station data (CHIRPS), and African Rainfall Climatology 2.0 (ARC2). The results were analyzed for two CMIP5 scenarios for the different time periods: assessment: 1983-2005; near future: 2006-2028; medium term: 2030-2052; and far future: 2077-2099). The validation results show that the models reproduce the annual cycle well. Except for the IPSL-CM5B model, which gives a peak in August, all the other models (ACCESS1-3, CanESM2, CSIRO, CMCC-CM, CMCC-CMS, CNRM-CM5, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, inmcm4, and IPSL-CM5B) agree with the validation data on a maximum peak in September with a period of strong transmission in August-October. With spatial variation, the CMIP5 model simulations show more of a difference in the number of malaria cases between the south and the north. Malaria transmission is much higher in the south than in the north. However, the results predicted by the models on the occurrence of malaria by 2100 show differences between the RCP8.5 scenario, considered a high emission scenario, and the RCP4.5 scenario, considered an intermediate mitigation scenario. The CanESM2, CMCC-CM, CMCC-CMS, inmcm4, and IPSL-CM5B models predict decreases with the RCP4.5 scenario. However, ACCESS1-3, CSIRO, NRCM-CM5, GFDL-CM3, GFDL-ESM2G, and GFDL-ESM2M predict increases in malaria under all scenarios (RCP4.5 and RCP8.5). The projected decrease in malaria in the future with these models is much more visible in the RCP8.5 scenario. The results of this study are of paramount importance in the climate-health field. These results will assist in decision-making and will allow for the establishment of preventive surveillance systems for local climate-sensitive diseases, including malaria, in the targeted regions of Senegal.
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Affiliation(s)
- Papa Fall
- Laboratoire Environnement-Ingénierie-Télécommunication-Energies Renouvelables (LEITER), Unité de Formation et de Recherche de Sciences Appliquées et de Technologie, Université Gaston Berger de Saint-Louis, BP 234, Saint-Louis 32000, Senegal
| | - Ibrahima Diouf
- Laboratoire de Physique de l'Atmosphère et de l'Océan-Siméon Fongang, Ecole Supérieure Polytechnique de l'Université Cheikh Anta Diop (UCAD), BP 5085, Dakar-Fann, Dakar 10700, Senegal
| | - Abdoulaye Deme
- Laboratoire Environnement-Ingénierie-Télécommunication-Energies Renouvelables (LEITER), Unité de Formation et de Recherche de Sciences Appliquées et de Technologie, Université Gaston Berger de Saint-Louis, BP 234, Saint-Louis 32000, Senegal
| | - Semou Diouf
- Laboratoire Environnement-Ingénierie-Télécommunication-Energies Renouvelables (LEITER), Unité de Formation et de Recherche de Sciences Appliquées et de Technologie, Université Gaston Berger de Saint-Louis, BP 234, Saint-Louis 32000, Senegal
| | - Doudou Sene
- Programme National de Lutte Contre le Paludisme (PNLP), BP 5085, Dakar-Fann, Dakar 10700, Senegal
| | - Benjamin Sultan
- ESPACE-DEV, Université Montpellier, IRD, Université Guyane, Université Réunion, Université Antilles, Université Avignon, 34093 Montpellier, France
| | - Adjoua Moïse Famien
- Laboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques (LOCEAN), Sorbonne Université, IRD, CNRS, MNHN, 75005 Paris, France
- Département de Sciences et Techniques, Université Alassane Ouattara de Bouaké, Bouaké 01 BPV 18, Côte d'Ivoire
| | - Serge Janicot
- Laboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques (LOCEAN), Sorbonne Université, IRD, CNRS, MNHN, 75005 Paris, France
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5
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Barrile GM, Augustine DJ, Porensky LM, Duchardt CJ, Shoemaker KT, Hartway CR, Derner JD, Hunter EA, Davidson AD. A big data-model integration approach for predicting epizootics and population recovery in a keystone species. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2023; 33:e2827. [PMID: 36846939 DOI: 10.1002/eap.2827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 11/21/2022] [Accepted: 01/10/2023] [Indexed: 06/02/2023]
Abstract
Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex nonlinear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a nonparametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, Cynomys ludovicianus) and sylvatic plague as an example. We synthesized colony data between 2001 and 2020 from eight USDA Forest Service National Grasslands across the range of BTPDs in central North America. We then modeled extinctions due to plague and colony recovery of BTPDs in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summers/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally >0.80). Thus, these spatially explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host-pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data-model integration approach provides a general framework for spatially explicit forecasting of disease-induced population fluctuations for use in natural resource management decision-making.
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Affiliation(s)
- Gabriel M Barrile
- Colorado Natural Heritage Program, Colorado State University, Fort Collins, Colorado, USA
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA
| | | | | | - Courtney J Duchardt
- Department of Natural Resource Ecology and Management, Oklahoma State University, Stillwater, Oklahoma, USA
| | - Kevin T Shoemaker
- Department of Natural Resources and Environmental Science, University of Nevada, Reno, Nevada, USA
| | | | | | - Elizabeth A Hunter
- U.S. Geological Survey, Virginia Cooperative Fish and Wildlife Research Unit, Department of Fisheries and Wildlife Conservation, Virginia Tech, Blacksburg, Virginia, USA
| | - Ana D Davidson
- Colorado Natural Heritage Program, Colorado State University, Fort Collins, Colorado, USA
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA
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6
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Benincà E, Pinto S, Cazelles B, Fuentes S, Shetty S, Bogaards JA. Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome. Sci Rep 2023; 13:8042. [PMID: 37198426 DOI: 10.1038/s41598-023-34713-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 05/05/2023] [Indexed: 05/19/2023] Open
Abstract
Human microbiome research is helped by the characterization of microbial networks, as these may reveal key microbes that can be targeted for beneficial health effects. Prevailing methods of microbial network characterization are based on measures of association, often applied to limited sampling points in time. Here, we demonstrate the potential of wavelet clustering, a technique that clusters time series based on similarities in their spectral characteristics. We illustrate this technique with synthetic time series and apply wavelet clustering to densely sampled human gut microbiome time series. We compare our results with hierarchical clustering based on temporal correlations in abundance, within and across individuals, and show that the cluster trees obtained by using either method are significantly different in terms of elements clustered together, branching structure and total branch length. By capitalizing on the dynamic nature of the human microbiome, wavelet clustering reveals community structures that remain obscured in correlation-based methods.
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Affiliation(s)
- Elisa Benincà
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.
| | - Susanne Pinto
- Biomedical Data Sciences, Leiden UMC, Leiden, The Netherlands
| | - Bernard Cazelles
- CNRS UMR-8197, IBENS, Ecole Normale Supérieure, Paris, France
- Sorbonne Université, UMMISCO, Paris, France
| | - Susana Fuentes
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Sudarshan Shetty
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Department of Medical Microbiology and Infection Prevention, UMC Groningen, Groningen, The Netherlands
| | - Johannes A Bogaards
- Department of Epidemiology & Data Science, Amsterdam UMC location VUMC, Amsterdam, The Netherlands
- Amsterdam Institute for Infection and Immunity, Amsterdam UMC, Amsterdam, The Netherlands
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7
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Kamana E, Bai D, Brown HE, Zhao J. The malaria transmission in Anhui province China. Infect Dis Model 2023; 8:1-10. [PMID: 36582746 PMCID: PMC9764179 DOI: 10.1016/j.idm.2022.11.009] [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: 09/07/2022] [Accepted: 11/22/2022] [Indexed: 11/28/2022] Open
Abstract
Plasmodium vivax and Plasmodium falciparum cases have opposite trends in Anhui China in the past decade. Long term and seasonal trends in the transmission rate of P. falciparum in Africa has been well studied, however that of P. vivax transmitted by Anopheles sinensis in China has not been investigated. There is a lot of work on the relationship between P. vivax cases and climatic factors in China, with sometimes contradicting results. However, how climatic factors affect transmission rate of P. vivax in China is unknown. We used Anhui province as an example to analyze the recent transmission dynamics where two types of malaria have been reported with differing etiologies. We examined breakpoints of the P. vivax and P. falciparum malaria long term dynamics in the recent decade. For locally transmitted P. vivax malaria, we analyzed the transmission rate and its seasonality using the combined human and mosquitos SIR-SI model with time-varied mosquito biting rate. We identified the effects of meteorological factors on the seasonality in transmission rate using a GAM model. For the imported P. falciparum malaria, we analyzed the potential reason for the observed increase in cases. The breakpoints of P. vivax and P. falciparum dynamics happened in a same year, 2010. The seasonality in the transmission rate of P. vivax malaria was high (42.4%) and was linearly associated with temperature and nonlinearly with rainfall. The abrupt increase in imported P. falciparum cases after the breakpoint was significantly related to the increased annual Chinese investment in Africa. Under the conditions of the existing vectors of malaria, long-term trends in climatic factors, and increasing trend in migration to/from endemic areas and imported malaria cases, we should be cautious of the possibility of the reestablishment of malaria in regions where it has been eliminated or the establishment of other vector-borne diseases.
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Affiliation(s)
- Eric Kamana
- Complexity Science Institute, Qingdao University, Qingdao, China
| | - Di Bai
- Complexity Science Institute, Qingdao University, Qingdao, China
| | - Heidi E. Brown
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, The University of Arizona, Tucson, AZ, USA
| | - Jijun Zhao
- Complexity Science Institute, Qingdao University, Qingdao, China
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Pourtois JD, Tallam K, Jones I, Hyde E, Chamberlin AJ, Evans MV, Ihantamalala FA, Cordier LF, Razafinjato BR, Rakotonanahary RJL, Tsirinomen'ny Aina A, Soloniaina P, Raholiarimanana SH, Razafinjato C, Bonds MH, De Leo GA, Sokolow SH, Garchitorena A. Climatic, land-use and socio-economic factors can predict malaria dynamics at fine spatial scales relevant to local health actors: Evidence from rural Madagascar. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0001607. [PMID: 36963091 PMCID: PMC10021226 DOI: 10.1371/journal.pgph.0001607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 01/23/2023] [Indexed: 02/24/2023]
Abstract
While much progress has been achieved over the last decades, malaria surveillance and control remain a challenge in countries with limited health care access and resources. High-resolution predictions of malaria incidence using routine surveillance data could represent a powerful tool to health practitioners by targeting malaria control activities where and when they are most needed. Here, we investigate the predictors of spatio-temporal malaria dynamics in rural Madagascar, estimated from facility-based passive surveillance data. Specifically, this study integrates climate, land-use, and representative household survey data to explain and predict malaria dynamics at a high spatial resolution (i.e., by Fokontany, a cluster of villages) relevant to health care practitioners. Combining generalized linear mixed models (GLMM) and path analyses, we found that socio-economic, land use and climatic variables are all important predictors of monthly malaria incidence at fine spatial scales, via both direct and indirect effects. In addition, out-of-sample predictions from our model were able to identify 58% of the Fokontany in the top quintile for malaria incidence and account for 77% of the variation in the Fokontany incidence rank. These results suggest that it is possible to build a predictive framework using environmental and social predictors that can be complementary to standard surveillance systems and help inform control strategies by field actors at local scales.
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Affiliation(s)
- Julie D Pourtois
- Biology Department, Stanford University, Stanford, CA, United States of America
- Hopkins Marine Station, Stanford University, Pacific Grove, CA, United States of America
| | - Krti Tallam
- Biology Department, Stanford University, Stanford, CA, United States of America
| | - Isabel Jones
- Biology Department, Stanford University, Stanford, CA, United States of America
- Hopkins Marine Station, Stanford University, Pacific Grove, CA, United States of America
| | - Elizabeth Hyde
- School of Medicine, Stanford University, Stanford, CA, United States of America
| | - Andrew J Chamberlin
- Hopkins Marine Station, Stanford University, Pacific Grove, CA, United States of America
| | - Michelle V Evans
- MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France
| | - Felana A Ihantamalala
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, United States of America
- NGO Pivot, Ifanadiana, Madagascar
| | | | | | - Rado J L Rakotonanahary
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, United States of America
- NGO Pivot, Ifanadiana, Madagascar
| | | | | | | | - Celestin Razafinjato
- Programme National de Lutte contre le Paludisme, Ministère de la Santé Publique, Antananarivo, Madagascar
| | - Matthew H Bonds
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, United States of America
- NGO Pivot, Ifanadiana, Madagascar
| | - Giulio A De Leo
- Biology Department, Stanford University, Stanford, CA, United States of America
- Hopkins Marine Station, Stanford University, Pacific Grove, CA, United States of America
| | - Susanne H Sokolow
- Woods Institute for the Environment, Stanford University, Stanford, CA, United States of America
- Marine Science Institute and Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, CA, United States of America
| | - Andres Garchitorena
- MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France
- NGO Pivot, Ifanadiana, Madagascar
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9
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Krsulovic FAM, Moulton TP, Lima M, Jaksic F. Epidemic malaria dynamics in Ethiopia: the role of self-limiting, poverty, HIV, climate change and human population growth. Malar J 2022; 21:135. [PMID: 35477448 PMCID: PMC9044619 DOI: 10.1186/s12936-022-04161-2] [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: 07/27/2021] [Accepted: 04/11/2022] [Indexed: 11/10/2022] Open
Abstract
Background During the last two decades, researchers have suggested that the changes of malaria cases in African highlands were driven by climate change. Recently, a study claimed that the malaria cases (Plasmodium falciparum) in Oromia (Ethiopia) were related to minimum temperature. Critics highlighted that other variables could be involved in the dynamics of the malaria. The literature mentions that beyond climate change, trends in malaria cases could be involved with HIV, human population size, poverty, investments in health control programmes, among others. Methods Population ecologists have developed a simple framework, which helps to explore the contributions of endogenous (density-dependent) and exogenous processes on population dynamics. Both processes may operate to determine the dynamic behaviour of a particular population through time. Briefly, density-dependent (endogenous process) occurs when the per capita population growth rate (R) is determined by the previous population size. An exogenous process occurs when some variable affects another but is not affected by the changes it causes. This study explores the dynamics of malaria cases (Plasmodium falciparum and Plasmodium vivax) in Oromia region in Ethiopia and explores the interaction between minimum temperature, HIV, poverty, human population size and social instability. Results The results support that malaria dynamics showed signs of a negative endogenous process between R and malaria infectious class, and a weak evidence to support the climate change hypothesis. Conclusion Poverty, HIV, population size could interact to force malaria models parameters explaining the dynamics malaria observed at Ethiopia from 1985 to 2007.
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Affiliation(s)
- Felipe Augusto Maurin Krsulovic
- Departamento de Ecología, Facultad de Ciencias Biológicas, Pontifícia Universidad Católica de Chile, Santiago, Chile. .,Center of Applied Ecology and Sustainability (CAPES), Santiago, Chile.
| | - Timothy Peter Moulton
- Departamento de Ecologia, Faculdade de Ciéncias Biológicas, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brasil
| | - Mauricio Lima
- Departamento de Ecología, Facultad de Ciencias Biológicas, Pontifícia Universidad Católica de Chile, Santiago, Chile.,Center of Applied Ecology and Sustainability (CAPES), Santiago, Chile
| | - Fabian Jaksic
- Departamento de Ecología, Facultad de Ciencias Biológicas, Pontifícia Universidad Católica de Chile, Santiago, Chile.,Center of Applied Ecology and Sustainability (CAPES), Santiago, Chile
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10
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Morris AL, Ghani A, Ferguson N. Fine-scale estimation of key life-history parameters of malaria vectors: implications for next-generation vector control technologies. Parasit Vectors 2021; 14:311. [PMID: 34103094 PMCID: PMC8188720 DOI: 10.1186/s13071-021-04789-0] [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: 11/20/2020] [Accepted: 05/11/2021] [Indexed: 11/12/2022] Open
Abstract
Background Mosquito control has the potential to significantly reduce malaria burden on a region, but to influence public health policy must also show cost-effectiveness. Gaps in our knowledge of mosquito population dynamics mean that mathematical modelling of vector control interventions have typically made simplifying assumptions about key aspects of mosquito ecology. Often, these assumptions can distort the predicted efficacy of vector control, particularly next-generation tools such as gene drive, which are highly sensitive to local mosquito dynamics. Methods We developed a discrete-time stochastic mathematical model of mosquito population dynamics to explore the fine-scale behaviour of egg-laying and larval density dependence on parameter estimation. The model was fitted to longitudinal mosquito population count data using particle Markov chain Monte Carlo methods. Results By modelling fine-scale behaviour of egg-laying under varying density dependence scenarios we refine our life history parameter estimates, and in particular we see how model assumptions affect population growth rate (Rm), a crucial determinate of vector control efficacy. Conclusions Subsequent application of these new parameter estimates to gene drive models show how the understanding and implementation of fine-scale processes, when deriving parameter estimates, may have a profound influence on successful vector control. The consequences of this may be of crucial interest when devising future public health policy. Graphic abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s13071-021-04789-0.
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Affiliation(s)
- Aaron L Morris
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK.
| | - Azra Ghani
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
| | - Neil Ferguson
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
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VilÀ M, Dunn AM, Essl F, GÓmez-DÍaz E, Hulme PE, Jeschke JM, NÚÑez MA, Ostfeld RS, Pauchard A, Ricciardi A, Gallardo B. Viewing Emerging Human Infectious Epidemics through the Lens of Invasion Biology. Bioscience 2021. [DOI: 10.1093/biosci/biab047] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Invasion biology examines species originated elsewhere and moved with the help of humans, and those species’ impacts on biodiversity, ecosystem services, and human well-being. In a globalized world, the emergence and spread of many human infectious pathogens are quintessential biological invasion events. Some macroscopic invasive species themselves contribute to the emergence and transmission of human infectious agents. We review conceptual parallels and differences between human epidemics and biological invasions by animals and plants. Fundamental concepts in invasion biology regarding the interplay of propagule pressure, species traits, biotic interactions, eco-evolutionary experience, and ecosystem disturbances can help to explain transitions between stages of epidemic spread. As a result, many forecasting and management tools used to address epidemics could be applied to biological invasions and vice versa. Therefore, we advocate for increasing cross-fertilization between the two disciplines to improve prediction, prevention, treatment, and mitigation of invasive species and infectious disease outbreaks, including pandemics.
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Affiliation(s)
- Montserrat VilÀ
- Department of Plant Biology and Ecology, University of Sevilla, Sevilla, Spain
| | | | - Franz Essl
- Department of Botany and Biodiversity Research, University of Vienna, Vienna, Austria
| | - Elena GÓmez-DÍaz
- Institute of Parasitology and Biomedicine Lopez-Neyra, Granada, Spain
| | - Philip E Hulme
- Bio-Protection Research Centre, Lincoln University, Canterbury, New Zealand
| | - Jonathan M Jeschke
- Leibniz Institute of Freshwater Ecology and Inland Fisheries, with the Institute of Biology, Freie Universität Berlin, and with the Berlin-Brandenburg Institute of Advanced Biodiversity Research, Berlin, Germany
| | - MartÍn A NÚÑez
- Department of Biology and Biochemistry, University of Houston, Houston, Texas, United States
| | - Richard S Ostfeld
- Cary Institute of Ecosystem Studies, Millbrook, New York, United States
| | - AnÍbal Pauchard
- Laboratorio de Invasiones Biológicas, Facultad de Ciencias Forestales, Universidad de Concepción, Concepción, Chile, and with the Institute of Ecology and Biodiversity, Santiago, Chile
| | | | - Belinda Gallardo
- Pyrenean Institute of Ecology, Zaragoza, Spain, and with the BioRISC (Biosecurity Research Initiative at St Catharine's), at St Catharine's College, Cambridge, United Kingdom
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12
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Ghanbarnejad A, Turki H, Yaseri M, Raeisi A, Rahimi-Foroushani A. Spatial Modelling of Malaria in South of Iran in Line with the Implementation of the Malaria Elimination Program: A Bayesian Poisson-Gamma Random Field Model. J Arthropod Borne Dis 2021; 15:108-125. [PMID: 34277860 PMCID: PMC8271232 DOI: 10.18502/jad.v15i1.6490] [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: 08/13/2020] [Accepted: 03/30/2021] [Indexed: 12/07/2022] Open
Abstract
Background: Malaria is the third most important infectious disease in the world. WHO propose programs for controlling and elimination of the disease. Malaria elimination program has begun in first phase in Iran from 2010. Climate factors play an important role in transmission and occurrence of malaria infection. The main goal is to investigate the spatial distribution of incidence of malaria during April 2011 to March 2018 in Hormozgan Province and its association with climate covariates. Methods: The data included 882 confirmed cases gathered from CDC in Hormozgan University of Medical Sciences. A Poisson-Gamma Random field model with Bayesian approach was used for modeling the data and produces the smoothed standardized incidence rate (SIR). Results: The SIR for malaria ranged from 0 (Abu Musa and Haji Abad districts) to 280.57 (Bandar–e-Jask). Based on model, temperature (RR= 2.29; 95% credible interval: (1.92–2.78)) and humidity (RR= 1.04; 95% credible interval: (1.03–1.06)) had positive effect on malaria incidence, but rainfall (RR= 0.92; 95% credible interval: (0.90–0.95)) had negative impact. Also, smoothed map represent hot spots in the east of the province and in Qeshm Island. Conclusion: Based on the analysis of the study results, it was found that the ecological conditions of the region (temperature, humidity and rainfall) and population displacement play an important role in the incidence of malaria. Therefore, the malaria surveillance system should continue to be active in the region, focusing on high-risk areas of malaria.
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Affiliation(s)
- Amin Ghanbarnejad
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Habibollah Turki
- Infectious and Tropical Diseases Research Center, Hormozgan Health Institute, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
| | - Mehdi Yaseri
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Ahmad Raeisi
- Departments of Medical Parasitology and Mycology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.,Center for Communicable Diseases Control, Ministry of Health and Medical Education, Tehran, Iran
| | - Abbas Rahimi-Foroushani
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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13
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Ryan SJ, Lippi CA, Zermoglio F. Shifting transmission risk for malaria in Africa with climate change: a framework for planning and intervention. Malar J 2020; 19:170. [PMID: 32357890 PMCID: PMC7193356 DOI: 10.1186/s12936-020-03224-6] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Accepted: 04/06/2020] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Malaria continues to be a disease of massive burden in Africa, and the public health resources targeted at surveillance, prevention, control, and intervention comprise large outlays of expense. Malaria transmission is largely constrained by the suitability of the climate for Anopheles mosquitoes and Plasmodium parasite development. Thus, as climate changes, shifts in geographic locations suitable for transmission, and differing lengths of seasons of suitability will occur, which will require changes in the types and amounts of resources. METHODS The shifting geographic risk of malaria transmission was mapped, in context of changing seasonality (i.e. endemic to epidemic, and vice versa), and the number of people affected. A published temperature-dependent model of malaria transmission suitability was applied to continental gridded climate data for multiple future AR5 climate model projections. The resulting outcomes were aligned with programmatic needs to provide summaries at national and regional scales for the African continent. Model outcomes were combined with population projections to estimate the population at risk at three points in the future, 2030, 2050, and 2080, under two scenarios of greenhouse gas emissions (RCP4.5 and RCP8.5). RESULTS Estimated geographic shifts in endemic and seasonal suitability for malaria transmission were observed across all future scenarios of climate change. The worst-case regional scenario (RCP8.5) of climate change predicted an additional 75.9 million people at risk from endemic (10-12 months) exposure to malaria transmission in Eastern and Southern Africa by the year 2080, with the greatest population at risk in Eastern Africa. Despite a predominance of reduction in season length, a net gain of 51.3 million additional people is predicted be put at some level of risk in Western Africa by midcentury. CONCLUSIONS This study provides an updated view of potential malaria geographic shifts in Africa under climate change for the more recent climate model projections (AR5), and a tool for aligning findings with programmatic needs at key scales for decision-makers. In describing shifting seasonality, it was possible to capture transitions between endemic and epidemic risk areas, to facilitate the planning for interventions aimed at year-round risk versus anticipatory surveillance and rapid response to potential outbreak locations.
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Affiliation(s)
- Sadie J Ryan
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.
- Department of Geography, University of Florida, Gainesville, FL, USA.
- College of Agriculture, Engineering, and Science, University of KwaZulu-Natal, Durban, South Africa.
| | - Catherine A Lippi
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
- Department of Geography, University of Florida, Gainesville, FL, USA
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Baghbanzadeh M, Kumar D, Yavasoglu SI, Manning S, Hanafi-Bojd AA, Ghasemzadeh H, Sikder I, Kumar D, Murmu N, Haque U. Malaria epidemics in India: Role of climatic condition and control measures. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 712:136368. [PMID: 32050403 DOI: 10.1016/j.scitotenv.2019.136368] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 12/24/2019] [Accepted: 12/25/2019] [Indexed: 05/28/2023]
Abstract
Malaria is a major public health problem in India, which is the second most populous country in the world. This study aimed to investigate the impact of climatic parameters and malaria control efforts implemented by the Indian national malaria control program on malaria epidemics between January of 2009 and December of 2015. A chi-squared test was used to study the correlation of all implemented control methods with occurrence of epidemics within 30, 45, 60 and 90 days and in the same district, 50, 100 and 200 km distance radiuses. The effect of each control method on probability of epidemics was also measured, and the effects of district population, season, and incidence of malaria parasite types were evaluated using logistic regression models. Fever survey was found to be effective for decreasing the odds of epidemics within 45, 60 and 90 days in 100 km. Anti-larval activity was also effective within 30, 45 and 60 days in 200 km. Winter had negative effects on odds ratio while summer and fall were more likely to trigger epidemics. These results contribute to understanding the role of climate variability and control efforts performed in India.
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Affiliation(s)
- Mahdi Baghbanzadeh
- Department of Business Development, Ofogh Kourosh Chain Stores, Tehran, Iran
| | - Dewesh Kumar
- Department of Preventive and Social Medicine, Rajendra Institute of Medical Sciences, Ranchi, India
| | - Sare I Yavasoglu
- Department of Biology, Faculty of Arts & Sciences, Aydin Adnan Menderes University, Aydin, Turkey
| | - Sydney Manning
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX 76107, USA
| | - Ahmad Ali Hanafi-Bojd
- Department of Medical Entomology and Vector Control, School of Public Health, Tehran School of Medical Science, Tehran, Iran
| | - Hassan Ghasemzadeh
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United States
| | - Ifthekar Sikder
- Department of Information System in Cleveland State University, USA
| | - Dilip Kumar
- Department of Preventive and Social Medicine, Rajendra Institute of Medical Sciences, Ranchi, India
| | - Nisha Murmu
- Department of Preventive and Social Medicine, Rajendra Institute of Medical Sciences, Ranchi, India
| | - Ubydul Haque
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX 76107, USA.
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15
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Gashaw KW, Kassa SM, Ouifki R. Climate-dependent malaria disease transmission model and its analysis. INT J BIOMATH 2019. [DOI: 10.1142/s1793524519500876] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Malaria infection continues to be a major problem in many parts of the world including Africa. Environmental variables are known to significantly affect the population dynamics and abundance of insects, major catalysts of vector-borne diseases, but the exact extent and consequences of this sensitivity are not yet well established. To assess the impact of the variability in temperature and rainfall on the transmission dynamics of malaria in a population, we propose a model consisting of a system of non-autonomous deterministic equations that incorporate the effect of both temperature and rainfall to the dispersion and mortality rate of adult mosquitoes. The model has been validated using epidemiological data collected from the western region of Ethiopia by considering the trends for the cases of malaria and the climate variation in the region. Further, a mathematical analysis is performed to assess the impact of temperature and rainfall change on the transmission dynamics of the model. The periodic variation of seasonal variables as well as the non-periodic variation due to the long-term climate variation have been incorporated and analyzed. In both periodic and non-periodic cases, it has been shown that the disease-free solution of the model is globally asymptotically stable when the basic reproduction ratio is less than unity in the periodic system and when the threshold function is less than unity in the non-periodic system. The disease is uniformly persistent when the basic reproduction ratio is greater than unity in the periodic system and when the threshold function is greater than unity in the non-periodic system.
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Affiliation(s)
| | - Semu Mitiku Kassa
- Department of Mathematics, Addis Ababa University, P. O. Box 1176, Addis Ababa, Ethiopia
- Department of Mathematics and Statistical Sciences, Botswana International University of Science and Technology (BIUST), P/Bag 16, Palapye, Botswana
| | - Rachid Ouifki
- Department of Mathematics and Applied Mathematics, University of Pretoria, South Africa
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16
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Rainfall Trends and Malaria Occurrences in Limpopo Province, South Africa. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16245156. [PMID: 31861127 PMCID: PMC6950450 DOI: 10.3390/ijerph16245156] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 12/05/2019] [Accepted: 12/08/2019] [Indexed: 02/01/2023]
Abstract
This contribution aims to investigate the influence of monthly total rainfall variations on malaria transmission in the Limpopo Province. For this purpose, monthly total rainfall was interpolated from daily rainfall data from weather stations. Annual and seasonal trends, as well as cross-correlation analyses, were performed on time series of monthly total rainfall and monthly malaria cases in five districts of Limpopo Province for the period of 1998 to 2017. The time series analysis indicated that an average of 629.5 mm of rainfall was received over the period of study. The rainfall has an annual variation of about 0.46%. Rainfall amount varied within the five districts, with the northeastern part receiving more rainfall. Spearman's correlation analysis indicated that the total monthly rainfall with one to two months lagged effect is significant in malaria transmission across all the districts. The strongest correlation was noticed in Vhembe (r = 0.54; p-value = <0.001), Mopani (r = 0.53; p-value = <0.001), Waterberg (r = 0.40; p-value =< 0.001), Capricorn (r = 0.37; p-value = <0.001) and lowest in Sekhukhune (r = 0.36; p-value = <0.001). Seasonally, the results indicated that about 68% variation in malaria cases in summer-December, January, and February (DJF)-can be explained by spring-September, October, and November (SON)-rainfall in Vhembe district. Both annual and seasonal analyses indicated that there is variation in the effect of rainfall on malaria across the districts and it is seasonally dependent. Understanding the dynamics of climatic variables annually and seasonally is essential in providing answers to malaria transmission among other factors, particularly with respect to the abrupt spikes of the disease in the province.
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Abiodun GJ, Makinde OS, Adeola AM, Njabo KY, Witbooi PJ, Djidjou-Demasse R, Botai JO. A Dynamical and Zero-Inflated Negative Binomial Regression Modelling of Malaria Incidence in Limpopo Province, South Africa. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16112000. [PMID: 31195637 PMCID: PMC6603991 DOI: 10.3390/ijerph16112000] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 06/03/2019] [Accepted: 06/03/2019] [Indexed: 11/16/2022]
Abstract
Recent studies have considered the connections between malaria incidence and climate variables using mathematical and statistical models. Some of the statistical models focused on time series approach based on Box-Jenkins methodology or on dynamic model. The latter approach allows for covariates different from its original lagged values, while the Box-Jenkins does not. In real situations, malaria incidence counts may turn up with many zero terms in the time series. Fitting time series model based on the Box-Jenkins approach and ARIMA may be spurious. In this study, a zero-inflated negative binomial regression model was formulated for fitting malaria incidence in Mopani and Vhembe-two of the epidemic district municipalities in Limpopo, South Africa. In particular, a zero-inflated negative binomial regression model was formulated for daily malaria counts as a function of some climate variables, with the aim of identifying the model that best predicts reported malaria cases. Results from this study show that daily rainfall amount and the average temperature at various lags have a significant influence on malaria incidence in the study areas. The significance of zero inflation on the malaria count was examined using the Vuong test and the result shows that zero-inflated negative binomial regression model fits the data better. A dynamical climate-based model was further used to investigate the population dynamics of mosquitoes over the two regions. Findings highlight the significant roles of Anopheles arabiensis on malaria transmission over the regions and suggest that vector control activities should be intense to eradicate malaria in Mopani and Vhembe districts. Although An. arabiensis has been identified as the major vector over these regions, our findings further suggest the presence of additional vectors transmitting malaria in the study regions. The findings from this study offer insight into climate-malaria incidence linkages over Limpopo province of South Africa.
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Affiliation(s)
- Gbenga J Abiodun
- Research Unit, Foundation for Professional Development, Pretoria 0040, South Africa.
- Department of Mathematics and Applied Mathematics, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa.
| | - Olusola S Makinde
- Department of Statistics, Federal University of Technology, Akure P.M.B 704, Nigeria.
| | - Abiodun M Adeola
- South African Weather Service, Private Bag X097, Pretoria 0001, South Africa.
- School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, Pretoria 0002, South Africa.
| | - Kevin Y Njabo
- Institute of the Environment and Sustainability, University of California Los Angeles, Los Angeles, CA 90095, USA.
| | - Peter J Witbooi
- Department of Mathematics and Applied Mathematics, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa.
| | | | - Joel O Botai
- South African Weather Service, Private Bag X097, Pretoria 0001, South Africa.
- Department of Geography, Geoinformation and Meteorology, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa.
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18
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Umer MF, Zofeen S, Majeed A, Hu W, Qi X, Zhuang G. Effects of Socio-Environmental Factors on Malaria Infection in Pakistan: A Bayesian Spatial Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E1365. [PMID: 30995744 PMCID: PMC6517989 DOI: 10.3390/ijerph16081365] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 04/07/2019] [Accepted: 04/13/2019] [Indexed: 12/04/2022]
Abstract
The role of socio-environmental factors in shaping malaria dynamics is complex and inconsistent. Effects of socio-environmental factors on malaria in Pakistan at district level were examined. Annual malaria cases data were obtained from Directorate of Malaria Control Program, Pakistan. Meteorological data were supplied by Pakistan Meteorological Department. A major limitation was the use of yearly, rather than monthly/weekly malaria data in this study. Population data, socio-economic data and education score data were downloaded from internet. Bayesian conditional autoregressive model was used to find the statistical association of socio-environmental factors with malaria in Pakistan. From 136/146 districts in Pakistan, >750,000 confirmed malaria cases were included, over a three years' period (2013-2015). Socioeconomic status ((posterior mean value -3.965, (2.5% quintile, -6.297%), (97.5% quintile, -1.754%)) and human population density (-7.41 × 10-4, -0.001406%, -1.05 × 10-4 %) were inversely related, while minimum temperature (0.1398, 0.05275%, 0.2145%) was directly proportional to malaria in Pakistan during the study period. Spatial random effect maps presented that moderate relative risk (RR, 0.75 to 1.24) and high RR (1.25 to 1.99) clusters were scattered throughout the country, outnumbering the ones' with low RR (0.23 to 0.74). Socio-environmental variables influence annual malaria incidence in Pakistan and needs further evaluation.
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Affiliation(s)
- Muhammad Farooq Umer
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an 710061, China.
| | - Shumaila Zofeen
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an 710061, China.
| | - Abdul Majeed
- Directorate of Malaria Control Program, Islamabad 44000, Pakistan.
| | - Wenbiao Hu
- School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, QLD 4059, Australia.
| | - Xin Qi
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an 710061, China.
- Global Health Institute, Xi'an Jiaotong University Health Science Center, Xi'an 710061, China.
| | - Guihua Zhuang
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an 710061, China.
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Malaria Risk Stratification and Modeling the Effect of Rainfall on Malaria Incidence in Eritrea. JOURNAL OF ENVIRONMENTAL AND PUBLIC HEALTH 2019; 2019:7314129. [PMID: 31061663 PMCID: PMC6466923 DOI: 10.1155/2019/7314129] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Accepted: 02/24/2019] [Indexed: 11/18/2022]
Abstract
Background Malaria risk stratification is essential to differentiate areas with distinct malaria intensity and seasonality patterns. The development of a simple prediction model to forecast malaria incidence by rainfall offers an opportunity for early detection of malaria epidemics. Objectives To construct a national malaria stratification map, develop prediction models and forecast monthly malaria incidences based on rainfall data. Methods Using monthly malaria incidence data from 2012 to 2016, the district level malaria stratification was constructed by nonhierarchical clustering. Cluster validity was examined by the maximum absolute coordinate change and analysis of variance (ANOVA) with a conservative post hoc test (Bonferroni) as the multiple comparison test. Autocorrelation and cross-correlation analyses were performed to detect the autocorrelation of malaria incidence and the lagged effect of rainfall on malaria incidence. The effect of rainfall on malaria incidence was assessed using seasonal autoregressive integrated moving average (SARIMA) models. Ljung-Box statistics for model diagnosis and stationary R-squared and Normalized Bayesian Information Criteria for model fit were used. Model validity was assessed by analyzing the observed and predicted incidences using the spearman correlation coefficient and paired samples t-test. Results A four cluster map (high risk, moderate risk, low risk, and very low risk) was the most valid stratification system for the reported malaria incidence in Eritrea. Monthly incidences were influenced by incidence rates in the previous months. Monthly incidence of malaria in the constructed clusters was associated with 1, 2, 3, and 4 lagged months of rainfall. The constructed models had acceptable accuracy as 73.1%, 46.3%, 53.4%, and 50.7% of the variance in malaria transmission were explained by rainfall in the high-risk, moderate-risk, low-risk, and very low-risk clusters, respectively. Conclusion Change in rainfall patterns affect malaria incidence in Eritrea. Using routine malaria case reports and rainfall data, malaria incidences can be forecasted with acceptable accuracy. Further research should consider a village or health facility level modeling of malaria incidence by including other climatic factors like temperature and relative humidity.
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Rouamba T, Nakanabo-Diallo S, Derra K, Rouamba E, Kazienga A, Inoue Y, Ouédraogo EK, Waongo M, Dieng S, Guindo A, Ouédraogo B, Sallah KL, Barro S, Yaka P, Kirakoya-Samadoulougou F, Tinto H, Gaudart J. Socioeconomic and environmental factors associated with malaria hotspots in the Nanoro demographic surveillance area, Burkina Faso. BMC Public Health 2019; 19:249. [PMID: 30819132 PMCID: PMC6396465 DOI: 10.1186/s12889-019-6565-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 02/19/2019] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND With limited resources and spatio-temporal heterogeneity of malaria in developing countries, it is still difficult to assess the real impact of socioeconomic and environmental factors in order to set up targeted campaigns against malaria at an accurate scale. Our goal was to detect malaria hotspots in rural area and assess the extent to which household socioeconomic status and meteorological recordings may explain the occurrence and evolution of these hotspots. METHODS Data on malaria cases from 2010 to 2014 and on socioeconomic and meteorological factors were acquired from four health facilities within the Nanoro demographic surveillance area. Statistical cross correlation was used to quantify the temporal association between weekly malaria incidence and meteorological factors. Local spatial autocorrelation analysis was performed and restricted to each transmission period using Kulldorff's elliptic spatial scan statistic. Univariate and multivariable analysis were used to assess the principal socioeconomic and meteorological determinants of malaria hotspots using a Generalized Estimating Equation (GEE) approach. RESULTS Rainfall and temperature were positively and significantly associated with malaria incidence, with a lag time of 9 and 14 weeks, respectively. Spatial analysis showed a spatial autocorrelation of malaria incidence and significant hotspots which was relatively stable throughout the study period. Furthermore, low socioeconomic status households were strongly associated with malaria hotspots (aOR = 1.21, 95% confidence interval: 1.03-1.40). CONCLUSION These fine-scale findings highlight a relatively stable spatio-temporal pattern of malaria risk and indicate that social and environmental factors play an important role in malaria incidence. Integrating data on these factors into existing malaria struggle tools would help in the development of sustainable bottleneck strategies adapted to the local context for malaria control.
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Affiliation(s)
- Toussaint Rouamba
- Clinical Research Unit of Nanoro, Institute for Research in Health Sciences, National Center for Scientific and Technological Research, Nanoro, Burkina Faso
- Aix Marseille Univ, IRD, INSERM, UMR1252 Sciences Economiques & Sociales de la Santé & Traitement de l’Information Médicale, Marseille, France
- Center for Research in Epidemiology, Biostatistics and Clinical Research, School of Public Health, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Seydou Nakanabo-Diallo
- Clinical Research Unit of Nanoro, Institute for Research in Health Sciences, National Center for Scientific and Technological Research, Nanoro, Burkina Faso
| | - Karim Derra
- Clinical Research Unit of Nanoro, Institute for Research in Health Sciences, National Center for Scientific and Technological Research, Nanoro, Burkina Faso
| | - Eli Rouamba
- Clinical Research Unit of Nanoro, Institute for Research in Health Sciences, National Center for Scientific and Technological Research, Nanoro, Burkina Faso
| | - Adama Kazienga
- Clinical Research Unit of Nanoro, Institute for Research in Health Sciences, National Center for Scientific and Technological Research, Nanoro, Burkina Faso
| | - Yasuko Inoue
- Aix Marseille Univ, IRD, INSERM, UMR1252 Sciences Economiques & Sociales de la Santé & Traitement de l’Information Médicale, Marseille, France
- Embassy of Japan in the Republic of Guinea, Conakry, Guinea
| | - Ernest K. Ouédraogo
- Direction Générale de la Météorologie du Burkina Faso, Ouagadougou, Burkina Faso
| | - Moussa Waongo
- Direction Générale de la Météorologie du Burkina Faso, Ouagadougou, Burkina Faso
| | - Sokhna Dieng
- Aix Marseille Univ, IRD, INSERM, UMR1252 Sciences Economiques & Sociales de la Santé & Traitement de l’Information Médicale, Marseille, France
- Ecole des Hautes Etudes en Santé Publique, Rennes, France
| | - Abdoulaye Guindo
- Aix Marseille Univ, IRD, INSERM, UMR1252 Sciences Economiques & Sociales de la Santé & Traitement de l’Information Médicale, Marseille, France
- MRTC, Malaria and Training Research Center – Ogobara Doumbo, Bamako, Mali
| | - Boukary Ouédraogo
- Aix Marseille Univ, IRD, INSERM, UMR1252 Sciences Economiques & Sociales de la Santé & Traitement de l’Information Médicale, Marseille, France
- Direction Régionale de la Santé du Centre-Ouest, Ministère de la santé, Koudougou, Burkina Faso
| | - Kankoé Lévi Sallah
- Aix Marseille Univ, IRD, INSERM, UMR1252 Sciences Economiques & Sociales de la Santé & Traitement de l’Information Médicale, Marseille, France
| | - Seydou Barro
- Directorate of Health Information Systems, Ministry of Health, Ouagadougou, Burkina Faso
| | - Pascal Yaka
- Direction Générale de la Météorologie du Burkina Faso, Ouagadougou, Burkina Faso
| | - Fati Kirakoya-Samadoulougou
- Center for Research in Epidemiology, Biostatistics and Clinical Research, School of Public Health, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Halidou Tinto
- Clinical Research Unit of Nanoro, Institute for Research in Health Sciences, National Center for Scientific and Technological Research, Nanoro, Burkina Faso
| | - Jean Gaudart
- Aix Marseille Univ, APHM, INSERM, IRD, SESSTIM, Hop Timone, BioSTIC, Marseille, France
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Wolfarth-Couto B, Silva RAD, Filizola N. Variability in malaria cases and the association with rainfall and rivers water levels in Amazonas State, Brazil. CAD SAUDE PUBLICA 2019; 35:e00020218. [PMID: 30758451 DOI: 10.1590/0102-311x00020218] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 09/05/2018] [Indexed: 01/30/2023] Open
Abstract
Understanding the relations between rainfall and river water levels and malaria cases can provide important clues on modulation of the disease in the context of local climatic variability. In order to demonstrate how these relations can vary in the same endemic space, a coherence and wavelet phase analysis was performed between environmental and epidemiological variables from 2003 to 2010 for 8 municipalities (counties) in the state of Amazonas, Brazil (Barcelos, Borba, Canutama, Carauari, Coari, Eirunepé, Humaitá, and São Gabriel da Cachoeira). The results suggest significant coherences, mainly on the scale of annual variability, but scales of less than 1 year and of 2 years were also found. The analyses show that malaria cases display a peak at approximately 1 and a half months before or after peak rainfall and on average 1-4 months after peak river water levels in most of the municipalities studied. Each environmental variable displayed distinct local behavior in time and in space, suggesting that other local variables (e.g. topography) may control environmental conditions, favoring different patterns in each municipality. However, when the analyses were performed jointly it was possible to show a non-random order in these relations. Although environmental and climatic factors indicate a certain influence on malaria dynamics, surveillance, prevention, and control issues should not be overlooked, meaning that government public health interventions can mask possible relations with local hydrological and climatic conditions.
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Impact of Weekly Climatic Variables on Weekly Malaria Incidence throughout Thailand: A Country-Based Six-Year Retrospective Study. JOURNAL OF ENVIRONMENTAL AND PUBLIC HEALTH 2018; 2018:8397815. [PMID: 30651742 PMCID: PMC6311806 DOI: 10.1155/2018/8397815] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 11/08/2018] [Accepted: 11/15/2018] [Indexed: 12/28/2022]
Abstract
Purpose. This study aimed to evaluate climatic data, including mean temperature, relative humidity, and rainfall, and their association with malaria incidence throughout Thailand from 2012 to 2017. The correlation of climatic parameters including temperature, relative humidity, and rainfall in each province and the weekly malaria incidence was analyzed using Spearman's rank correlation. The results showed that the mean temperature correlated with malaria incidence (p value < 0.05) in 44 provinces in Thailand. These correlations were frequently found in the western and southern parts of Thailand. Relative humidity correlated with malaria incidence (p value < 0.05) in 35 provinces. These correlations were frequently found in the northern and northeastern parts of Thailand. Rainfall correlated with malaria incidence (p value < 0.05) in 38 provinces. These correlations were frequently found in the northern parts and some western parts of Thailand. The impacts of the mean temperature, relative humidity, and rainfall were observed frequently in specific provinces, including Chiang Mai, Chiang Rai, Trat, Kanchanaburi, Ubonratchathani, and Si Sa Ket. This is the first study to report areas where climatic data are associated with malaria incidence throughout Thailand from 2012 to 2017. These results can map out the climatic change process over time and across the country, which is the foundation for effective early warning systems for malaria, public health awareness campaigns, and the adoption of proper adaption measures that will help in malaria detection, diagnosis, and treatment.
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23
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Haddawy P, Yin MS, Wisanrakkit T, Limsupavanich R, Promrat P, Lawpoolsri S, Sa-Angchai P. Complexity-Based Spatial Hierarchical Clustering for Malaria Prediction. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2018; 2:423-447. [PMID: 35415412 DOI: 10.1007/s41666-018-0031-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 07/17/2018] [Accepted: 07/18/2018] [Indexed: 11/24/2022]
Abstract
Targeted intervention and resource allocation are essential in effective control of infectious diseases, particularly those like malaria that tend to occur in remote areas. Disease prediction models can help support targeted intervention, particularly if they have fine spatial resolution. But, choosing an appropriate resolution is a difficult problem since choice of spatial scale can have a significant impact on accuracy of predictive models. In this paper, we introduce a new approach to spatial clustering for disease prediction we call complexity-based spatial hierarchical clustering. The technique seeks to find spatially compact clusters that have time series that can be well characterized by models of low complexity. We evaluate our approach with 2 years of malaria case data from Tak Province in northern Thailand. We show that the technique's use of reduction in Akaike information criterion (AIC) and Bayesian information criterion (BIC) as clustering criteria leads to rapid improvement in predictability and significantly better predictability than clustering based only on minimizing spatial intra-cluster distance for the entire range of cluster sizes over a variety of predictive models and prediction horizons.
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Affiliation(s)
- Peter Haddawy
- Faculty of ICT, Mahidol University, Nakhon Pathom, Thailand.,Bremen Spatial Cognition Center, University of Bremen, Bremen, Germany
| | - Myat Su Yin
- Faculty of ICT, Mahidol University, Nakhon Pathom, Thailand
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Hurtado LA, Rigg CA, Calzada JE, Dutary S, Bernal D, Koo SI, Chaves LF. Population Dynamics of Anopheles albimanus (Diptera: Culicidae) at Ipetí-Guna, a Village in a Region Targeted for Malaria Elimination in Panamá. INSECTS 2018; 9:insects9040164. [PMID: 30453469 PMCID: PMC6316695 DOI: 10.3390/insects9040164] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 07/16/2018] [Accepted: 07/17/2018] [Indexed: 11/20/2022]
Abstract
Anopheles albimanus Wiedemann is a major malaria vector in Mesoamerica and the Caribbean whose population dynamics, in response to changing environments, has been relatively poorly studied. Here, we present monthly adult and larvae data collected from May 2016 to December 2017 in Ipetí-Guna, a village within an area targeted for malaria elimination in the República de Panamá. During the study period we collected a total of 1678 Anopheles spp. mosquitoes (1602 adults and 76 larvae). Over 95% of the collected Anopheles spp. mosquitoes were An. albimanus. Using time series analysis techniques, we found that population dynamics of larvae and adults were not significantly correlated with each other at any time lag, though correlations were highest at one month lag between larvae and adults and four months lag between adults and larvae. Larvae population dynamics had cycles of three months and were sensitive to changes in temperature with 5 months lag, while adult abundance was correlated with itself (1 month lag) and with the normalized difference vegetation index (NDVI) with three months lag. A key observation from our study is the absence of both larvae and adults of An. albimanus between January and April from environments associated with Guna population’s daily activities, which suggests this time window could be the best time to implement elimination campaigns aimed at clearing Plasmodium spp. parasites from Guna populations using, for example, mass drug administration.
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Affiliation(s)
- Lisbeth Amarilis Hurtado
- Departamento de Análisis Epidemiológico y Bioestadísticas, Instituto Conmemorativo Gorgas de Estudios de la Salud, Apartado Postal 0816-02593, Panamá, Republic of Panama.
| | - Chystrie A Rigg
- Departamento de Investigación en Parasitología, Instituto Conmemorativo Gorgas de Estudios de la Salud, Apartado Postal 0816-02593, Panamá, Republic of Panama.
| | - José E Calzada
- Departamento de Investigación en Parasitología, Instituto Conmemorativo Gorgas de Estudios de la Salud, Apartado Postal 0816-02593, Panamá, Republic of Panama.
| | - Sahir Dutary
- Departamento de Análisis Epidemiológico y Bioestadísticas, Instituto Conmemorativo Gorgas de Estudios de la Salud, Apartado Postal 0816-02593, Panamá, Republic of Panama.
| | - Damaris Bernal
- Departamento de Investigación en Entomología Médica, Instituto Conmemorativo Gorgas de Estudios de la Salud, Apartado Postal 0816-02593, Panamá, Republic of Panama.
| | - Susana Isabel Koo
- Departamento de Investigación en Entomología Médica, Instituto Conmemorativo Gorgas de Estudios de la Salud, Apartado Postal 0816-02593, Panamá, Republic of Panama.
| | - Luis Fernando Chaves
- Instituto Costarricense de Investigación y Enseñanza en Nutrición y Salud (INCIENSA), Apartado Postal 4-2250, Tres Ríos, Cartago, Costa Rica.
- Programa de Investigación en Enfermedades Tropicales (PIET), Escuela de Medicina Veterinaria, Universidad Nacional, Apartado Postal 304-3000, Heredia, Costa Rica.
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25
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Cotterill GG, Cross PC, Middleton AD, Rogerson JD, Scurlock BM, du Toit JT. Hidden cost of disease in a free-ranging ungulate: brucellosis reduces mid-winter pregnancy in elk. Ecol Evol 2018; 8:10733-10742. [PMID: 30519402 PMCID: PMC6262735 DOI: 10.1002/ece3.4521] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 08/06/2018] [Accepted: 08/19/2018] [Indexed: 11/08/2022] Open
Abstract
Demonstrating disease impacts on the vital rates of free-ranging mammalian hosts typically requires intensive, long-term study. Evidence for chronic pathogens affecting reproduction but not survival is rare, but has the potential for wide-ranging effects. Accurately quantifying disease-associated reductions in fecundity is important for advancing theory, generating accurate predictive models, and achieving effective management. We investigated the impacts of brucellosis (Brucella abortus) on elk (Cervus canadensis) productivity using serological data from over 6,000 captures since 1990 in the Greater Yellowstone Ecosystem, USA. Over 1,000 of these records included known age and pregnancy status. Using Bayesian multilevel models, we estimated the age-specific pregnancy probabilities of exposed and naïve elk. We then used repeat-capture data to investigate the full effects of the disease on life history. Brucellosis exposure reduced pregnancy rates of elk captured in mid- and late-winter. In an average year, we found 60% of exposed 2-year-old elk were pregnant compared to 91% of their naïve counterparts (a 31 percentage point reduction, 89% HPDI = 20%-42%), whereas exposed 3- to 9-year-olds were 7 percentage points less likely to be pregnant than naïve elk of their same age (89% HPDI = 2%-11%). We found these reduced rates of pregnancy to be independent from disease-induced abortions, which afflict a portion of exposed elk. We estimate that the combination of reduced pregnancy by mid-winter and the abortions following mid-winter reduces the reproductive output of exposed female elk by 24%, which affects population dynamics to a similar extent as severe winters or droughts. Exposing hidden reproductive costs of disease is essential to avoid conflating them with the effects of climate and predation. Such reproductive costs cause complex population dynamics, and the magnitude of the effect we found should drive a strong selection gradient if there is heritable resistance.
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Affiliation(s)
| | - Paul C. Cross
- U.S. Geological SurveyNorthern Rocky Mountain Science CenterBozemanMontana
| | - Arthur D. Middleton
- Department of Environmental Science, Policy and ManagementUniversity of CaliforniaBerkeleyCalifornia
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26
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Abiodun GJ, Njabo KY, Witbooi PJ, Adeola AM, Fuller TL, Okosun KO, Makinde OS, Botai JO. Exploring the Influence of Daily Climate Variables on Malaria Transmission and Abundance of Anopheles arabiensis over Nkomazi Local Municipality, Mpumalanga Province, South Africa. JOURNAL OF ENVIRONMENTAL AND PUBLIC HEALTH 2018; 2018:3143950. [PMID: 30584427 PMCID: PMC6280252 DOI: 10.1155/2018/3143950] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Accepted: 09/02/2018] [Indexed: 01/10/2023]
Abstract
The recent resurgence of malaria incidence across epidemic regions in South Africa has been linked to climatic and environmental factors. An in-depth investigation of the impact of climate variability and mosquito abundance on malaria parasite incidence may therefore offer useful insight towards the control of this life-threatening disease. In this study, we investigate the influence of climatic factors on malaria transmission over Nkomazi Municipality. The variability and interconnectedness between the variables were analyzed using wavelet coherence analysis. Time-series analyses revealed that malaria cases significantly declined after the outbreak in early 2000, but with a slight increase from 2015. Furthermore, the wavelet coherence and time-lagged correlation analyses identified rainfall and abundance of Anopheles arabiensis as the major variables responsible for malaria transmission over the study region. The analysis further highlights a high malaria intensity with the variables from 1998-2002, 2004-2006, and 2010-2013 and a noticeable periodicity value of 256-512 days. Also, malaria transmission shows a time lag between one month and three months with respect to mosquito abundance and the different climatic variables. The findings from this study offer a better understanding of the importance of climatic factors on the transmission of malaria. The study further highlights the significant roles of An. arabiensis on malaria occurrence over Nkomazi. Implementing the mosquito model to predict mosquito abundance could provide more insight into malaria elimination or control in Africa.
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Affiliation(s)
- Gbenga J. Abiodun
- Research Unit, Foundation for Professional Development, Pretoria, South Africa
- Department of Mathematics and Applied Mathematics, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa
| | - Kevin Y. Njabo
- Institute of the Environment and Sustainability, University of California Los Angeles, Los Angeles, California, USA
| | - Peter J. Witbooi
- Department of Mathematics and Applied Mathematics, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa
| | - Abiodun M. Adeola
- South African Weather Service, Private Bag X097, Pretoria 0001, South Africa
- School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Trevon L. Fuller
- Institute of the Environment and Sustainability, University of California Los Angeles, Los Angeles, California, USA
| | - Kazeem O. Okosun
- Department of Mathematics, Vaal University of Technology, X021, Vanderbijlpark 1900, South Africa
| | - Olusola S. Makinde
- Department of Statistics, Federal University of Technology, P.M.B 704, Akure, Nigeria
| | - Joel O. Botai
- South African Weather Service, Private Bag X097, Pretoria 0001, South Africa
- Department of Geography, Geoinformation and Meteorology, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa
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27
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Abiodun GJ, Witbooi PJ, Okosun KO, Maharaj R. Exploring the Impact of Climate Variability on Malaria Transmission Using a Dynamic Mosquito-Human Malaria Model. ACTA ACUST UNITED AC 2018; 10:88-100. [PMID: 30906484 PMCID: PMC6430130 DOI: 10.2174/1874279301810010088] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Introduction: The reasons for malaria resurgence mostly in Africa are yet to be well understood. Although the causes are often linked to regional climate change, it is important to understand the impact of climate variability on the dynamics of the disease. However, this is almost impossible without adequate long-term malaria data over the study areas. Methods: In this study, we develop a climate-based mosquito-human malaria model to study malaria dynamics in the human population over KwaZulu-Natal, one of the epidemic provinces in South Africa, from 1970-2005. We compare the model output with available observed monthly malaria cases over the province from September 1999 to December 2003. We further use the model outputs to explore the relationship between the climate variables (rainfall and temperature) and malaria incidence over the province using principal component analysis, wavelet power spectrum and wavelet coherence analysis. The model produces a reasonable fit with the observed data and in particular, it captures all the spikes in malaria prevalence. Results: Our results highlight the importance of climate factors on malaria transmission and show the seasonality of malaria epidemics over the province. Results from the principal component analyses further suggest that, there are two principal factors associated with climates variables and the model outputs. One of the factors indicate high loadings on Susceptible, Exposed and Infected human, while the other is more correlated with Susceptible and Recovered humans. However, both factors reveal the inverse correlation between Susceptible-Infected and Susceptible-Recovered humans respectively. Through the spectrum analysis, we notice a strong annual cycle of malaria incidence over the province and ascertain a dominant of one year periodicity. Consequently, our findings indicate that an average of 0 to 120-day lag is generally noted over the study period, but the 120-day lag is more associated with temperature than rainfall. This is consistence with other results obtained from our analyses that malaria transmission is more tightly coupled with temperature than with rainfall in KwaZulu-Natal province.
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Affiliation(s)
- Gbenga J Abiodun
- Research Unit, Foundation for Professional Development, Pretoria 0184, Republic of South Africa.,Department of Mathematics and Applied Mathematics, University of the Western Cape, Private Bag X17, Bellville7535, Republic of South Africa
| | - Peter J Witbooi
- Department of Mathematics and Applied Mathematics, University of the Western Cape, Private Bag X17, Bellville7535, Republic of South Africa
| | - Kazeem O Okosun
- Department of Mathematics, Vaal University of Technology, X021, Vanderbijlpark, 1900, Republic of South Africa
| | - Rajendra Maharaj
- Office of Malaria Research, South African Medical Research Council, Durban, Republic of South Africa
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28
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Nanvyat N, Mulambalah CS, Barshep Y, Ajiji JA, Dakul DA, Tsingalia HM. Malaria transmission trends and its lagged association with climatic factors in the highlands of Plateau State, Nigeria. Trop Parasitol 2018; 8:18-23. [PMID: 29930902 PMCID: PMC5991042 DOI: 10.4103/tp.tp_35_17] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/07/2017] [Indexed: 12/18/2022] Open
Abstract
Background: Malaria is a serious disease and still remains a public health problem in many parts of Nigeria. Objectives: The aim of this study was to describe malaria transmission trends and analyzed the impact of climatic factors on malaria transmission in the highlands of Plateau State, Central Nigeria. Methods: The study was a retrospective survey which used archival data of climate parameters and medical case records on malaria. Rainfall, relative humidity, and temperature data were obtained from the nearest weather stations to the study locations from 1980 to 2015. Data on reported malaria cases were collected from general hospitals in the selected local government areas (LGAs) from 2003 to 2015. Generalized Additive Models were used to model trends in malaria incidences over time, and it is lagged association with climatic factors. Results: The results show a significant cyclical trend in malaria incidence in all the study areas (P < 0.001). The association between monthly malaria cases and mean monthly temperature, rainfall, and relative humidity show significant association at different time lags and locations. Conclusion: Our findings suggest that climatic factors are among the major determinants of malaria transmission in the highlands of Plateau state except in Jos-North LGA where the low model deviance explained (35.4%) could mean that there are other important factors driving malaria transmission in the area other than climatic factors.
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Affiliation(s)
- N Nanvyat
- Department of Zoology, Faculty of Natural Sciences, University of Jos, Jos, Plateau State, Nigeria.,Department of Biological Sciences, School of Biological and Physical Sciences, Moi University, Eldoret, Kenya
| | - C S Mulambalah
- Department of Medical Microbiology and Parasitology, School of Medicine, College of Health Sciences, Moi University, Eldoret, Kenya
| | - Y Barshep
- Department of Zoology, Faculty of Natural Sciences, University of Jos, Jos, Plateau State, Nigeria
| | - J A Ajiji
- Medical Services Department, Plateau State Ministry of Health, Jos, Plateau State, Nigeria
| | - D A Dakul
- Department of Zoology, Faculty of Natural Sciences, University of Jos, Jos, Plateau State, Nigeria
| | - H M Tsingalia
- Department of Biological Sciences, School of Biological and Physical Sciences, Moi University, Eldoret, Kenya
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29
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Eikenberry SE, Gumel AB. Mathematical modeling of climate change and malaria transmission dynamics: a historical review. J Math Biol 2018; 77:857-933. [PMID: 29691632 DOI: 10.1007/s00285-018-1229-7] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Revised: 03/16/2018] [Indexed: 12/24/2022]
Abstract
Malaria, one of the greatest historical killers of mankind, continues to claim around half a million lives annually, with almost all deaths occurring in children under the age of five living in tropical Africa. The range of this disease is limited by climate to the warmer regions of the globe, and so anthropogenic global warming (and climate change more broadly) now threatens to alter the geographic area for potential malaria transmission, as both the Plasmodium malaria parasite and Anopheles mosquito vector have highly temperature-dependent lifecycles, while the aquatic immature Anopheles habitats are also strongly dependent upon rainfall and local hydrodynamics. A wide variety of process-based (or mechanistic) mathematical models have thus been proposed for the complex, highly nonlinear weather-driven Anopheles lifecycle and malaria transmission dynamics, but have reached somewhat disparate conclusions as to optimum temperatures for transmission, and the possible effect of increasing temperatures upon (potential) malaria distribution, with some projecting a large increase in the area at risk for malaria, but others predicting primarily a shift in the disease's geographic range. More generally, both global and local environmental changes drove the initial emergence of P. falciparum as a major human pathogen in tropical Africa some 10,000 years ago, and the disease has a long and deep history through the present. It is the goal of this paper to review major aspects of malaria biology, methods for formalizing these into mathematical forms, uncertainties and controversies in proper modeling methodology, and to provide a timeline of some major modeling efforts from the classical works of Sir Ronald Ross and George Macdonald through recent climate-focused modeling studies. Finally, we attempt to place such mathematical work within a broader historical context for the "million-murdering Death" of malaria.
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Affiliation(s)
- Steffen E Eikenberry
- Global Security Initiative, Arizona State University, Tempe, AZ, USA. .,School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA.
| | - Abba B Gumel
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA
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30
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Ouedraogo B, Inoue Y, Kambiré A, Sallah K, Dieng S, Tine R, Rouamba T, Herbreteau V, Sawadogo Y, Ouedraogo LSLW, Yaka P, Ouedraogo EK, Dufour JC, Gaudart J. Spatio-temporal dynamic of malaria in Ouagadougou, Burkina Faso, 2011-2015. Malar J 2018; 17:138. [PMID: 29609606 PMCID: PMC5879937 DOI: 10.1186/s12936-018-2280-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Accepted: 03/21/2018] [Indexed: 11/25/2022] Open
Abstract
Background Given the scarcity of resources in developing countries, malaria treatment requires new strategies that target specific populations, time periods and geographical areas. While the spatial pattern of malaria transmission is known to vary depending on local conditions, its temporal evolution has yet to be evaluated. The aim of this study was to determine the spatio-temporal dynamic of malaria in the central region of Burkina Faso, taking into account meteorological factors. Methods Drawing on national databases, 101 health areas were studied from 2011 to 2015, together with weekly meteorological data (temperature, number of rain events, rainfall, humidity, wind speed). Meteorological factors were investigated using a principal component analysis (PCA) to reduce dimensions and avoid collinearities. The Box–Jenkins ARIMA model was used to test the stationarity of the time series. The impact of meteorological factors on malaria incidence was measured with a general additive model. A change-point analysis was performed to detect malaria transmission periods. For each transmission period, malaria incidence was mapped and hotspots were identified using spatial cluster detection. Results Malaria incidence never went below 13.7 cases/10,000 person-weeks. The first and second PCA components (constituted by rain/humidity and temperatures, respectively) were correlated with malaria incidence with a lag of 2 weeks. The impact of temperature was significantly non-linear: malaria incidence increased with temperature but declined sharply with high temperature. A significant positive linear trend was found for the entire time period. Three transmission periods were detected: low (16.8–29.9 cases/10,000 person-weeks), high (51.7–84.8 cases/10,000 person-weeks), and intermediate (26.7–32.2 cases/10,000 person-weeks). The location of clusters identified as high risk varied little across transmission periods. Conclusion This study highlighted the spatial variability and relative temporal stability of malaria incidence around the capital Ouagadougou, in the central region of Burkina Faso. Despite increasing efforts in fighting the disease, malaria incidence remained high and increased over the period of study. Hotspots, particularly those detected for low transmission periods, should be investigated further to uncover the local environmental and behavioural factors of transmission, and hence to allow for the development of better targeted control strategies. Electronic supplementary material The online version of this article (10.1186/s12936-018-2280-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Boukary Ouedraogo
- Aix Marseille Univ, INSERM, IRD, SESSTIM UMR1252 Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, Marseille, France.
| | - Yasuko Inoue
- Aix Marseille Univ, INSERM, IRD, SESSTIM UMR1252 Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, Marseille, France.,Embassy of Japan in the Republic of Guinea, Conakry, Guinea
| | - Alinsa Kambiré
- Aix Marseille Univ, INSERM, IRD, SESSTIM UMR1252 Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, Marseille, France
| | - Kankoe Sallah
- Aix Marseille Univ, INSERM, IRD, SESSTIM UMR1252 Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, Marseille, France.,Prospective et Coopération, Laboratoire d'Idées, Bureau d'Etudes Recherche, Marseille, France
| | - Sokhna Dieng
- Aix Marseille Univ, INSERM, IRD, SESSTIM UMR1252 Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, Marseille, France.,Ecole des Hautes Etudes en Santé Publique, Rennes, France
| | - Raphael Tine
- Aix Marseille Univ, INSERM, IRD, SESSTIM UMR1252 Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, Marseille, France
| | - Toussaint Rouamba
- Aix Marseille Univ, INSERM, IRD, SESSTIM UMR1252 Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, Marseille, France.,Université libre de Bruxelles, EPS, Centre de Recherche en Epidémiologie, Biostatistique et Recherche Clinique, Brussels, Belgium.,IRSS-Clinical Research Unit of Nanoro (IRSS-CRUN), Nanoro, Burkina Faso
| | | | - Yacouba Sawadogo
- Programme National de Lutte contre le Paludisme, Ministère de la Santé, Ouagadougou, Burkina Faso
| | | | - Pascal Yaka
- Direction de la Météorologie, Ministère des Transports, Ouagadougou, Burkina Faso
| | - Ernest K Ouedraogo
- Direction de la Météorologie, Ministère des Transports, Ouagadougou, Burkina Faso
| | - Jean-Charles Dufour
- Aix Marseille Univ, INSERM, IRD, SESSTIM UMR1252 Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, Marseille, France
| | - Jean Gaudart
- Aix Marseille Univ, INSERM, IRD, SESSTIM UMR1252 Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, Marseille, France
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Yue RPH, Lee HF. Pre-industrial plague transmission is mediated by the synergistic effect of temperature and aridity index. BMC Infect Dis 2018; 18:134. [PMID: 29554882 PMCID: PMC5859406 DOI: 10.1186/s12879-018-3045-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2017] [Accepted: 03/13/2018] [Indexed: 01/14/2023] Open
Abstract
Background Although the linkage between climate change and plague transmission has been proposed in previous studies, the dominant approach has been to address the linkage with traditional statistical methods, while the possible non-linearity, non-stationarity and low frequency domain of the linkage has not been fully considered. We seek to address the above issue by investigating plague transmission in pre-industrial Europe (AD1347–1760) at both continental and country levels. Methods We apply Granger Causality Analysis to identify the casual relationship between climatic variables and plague outbreaks. We then apply Wavelet Analysis to explore the non-linear and non-stationary association between climate change and plague outbreaks. Results Our results show that 5-year lagged temperature and aridity index are the significant determinants of plague outbreaks in pre-industrial Europe. At the multi-decadal time scale, there are more frequent plague outbreaks in a cold and arid climate. The synergy of temperature and aridity index, rather than their individual effect, is more imperative in driving plague outbreaks, which is valid at both the continental and country levels. Conclusions Plague outbreaks come after cold and dry spells. The multi-decadal climate variability is imperative in driving the cycles of plague outbreaks in pre-industrial Europe. The lagged and multi-decadal effect of climate change on plague outbreaks may be attributable to the complexity of ecological, social, or climate systems, through which climate exerts its influence on plague dynamics. These findings may contribute to improve our understanding of the epidemiology of plague and other rodent-borne or flea-borne infectious diseases in human history. Electronic supplementary material The online version of this article (10.1186/s12879-018-3045-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ricci P H Yue
- Department of Geography, The University of Hong Kong, Pok Fu Lam, Hong Kong.
| | - Harry F Lee
- Department of Geography, The University of Hong Kong, Pok Fu Lam, Hong Kong. .,International Center for China Development Studies, The University of Hong Kong, Pok Fu Lam, Hong Kong.
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Hurtado LA, Calzada JE, Rigg CA, Castillo M, Chaves LF. Climatic fluctuations and malaria transmission dynamics, prior to elimination, in Guna Yala, República de Panamá. Malar J 2018; 17:85. [PMID: 29463259 PMCID: PMC5819664 DOI: 10.1186/s12936-018-2235-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 02/13/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Malaria has historically been entrenched in indigenous populations of the República de Panamá. This scenario occurs despite the fact that successful methods for malaria elimination were developed during the creation of the Panamá Canal. Today, most malaria cases in the República de Panamá affect the Gunas, an indigenous group, which mainly live in autonomous regions of eastern Panamá. Over recent decades several malaria outbreaks have affected the Gunas, and one hypothesis is that such outbreaks could have been exacerbated by climate change, especially by anomalous weather patterns driven by the EL Niño Southern Oscillation (ENSO). RESULTS Monthly malaria cases in Guna Yala (1998-2016) were autocorrelated up to 2 months of lag, likely reflecting parasite transmission cycles between humans and mosquitoes, and cyclically for periods of 4 months that might reflect relapses of Plasmodium vivax, the dominant malaria parasite transmitted in Panamá. Moreover, malaria case number was positively associated (P < 0.05) with rainfall (7 months of lag), and negatively with the El Niño 4 index (15 months of lag) and the Normalized Difference Vegetation Index, NDVI (8 months of lag), the sign and magnitude of these associations likely related to the impacts of weather patterns and vegetation on the ecology of Anopheles albimanus, the main malaria vector in Guna Yala. Interannual cycles, of approximately 4-year periods, in monthly malaria case numbers were associated with the El Niño 4 index, a climatic index associated with weather and vegetation dynamics in Guna Yala at seasonal and interannual time scales. CONCLUSION The results showed that ENSO, rainfall and NDVI were associated with the number of malaria cases in Guna Yala during the study period. These results highlight the vulnerability of Guna populations to malaria, an infection sensitive to climate change, and call for further studies about weather impacts on malaria vector ecology, as well as the association of malaria vectors with Gunas paying attention to their socio-economic conditions of poverty and cultural differences as an ethnic minority.
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Affiliation(s)
- Lisbeth Amarilis Hurtado
- Unidad de Análisis Epidemiológico y Bioestadísticas, Instituto Commemorativo Gorgas de Estudios de la Salud, Panamá, República de Panamá
| | - José E Calzada
- Departamento de Investigación en Parasitología, Instituto Commemorativo Gorgas de Estudios de la Salud, Panamá, República de Panamá
| | - Chystrie A Rigg
- Departamento de Investigación en Parasitología, Instituto Commemorativo Gorgas de Estudios de la Salud, Panamá, República de Panamá
| | - Milagros Castillo
- Unidad de Análisis Epidemiológico y Bioestadísticas, Instituto Commemorativo Gorgas de Estudios de la Salud, Panamá, República de Panamá
| | - Luis Fernando Chaves
- Instituto Costarricense de Investigación y Enseñanza en Nutrición y Salud (INCIENSA), Apartado 4-2250, Tres Ríos, Cartago, Costa Rica.
- Programa de Investigación en Enfermedades Tropicales (PIET), Escuela de Medicina Veterinaria, Universidad Nacional, Apartado Postal 304-3000, Heredia, Costa Rica.
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Wu DF, Löhrich T, Sachse A, Mundry R, Wittig RM, Calvignac-Spencer S, Deschner T, Leendertz FH. Seasonal and inter-annual variation of malaria parasite detection in wild chimpanzees. Malar J 2018; 17:38. [PMID: 29347985 PMCID: PMC5774132 DOI: 10.1186/s12936-018-2187-7] [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: 07/07/2017] [Accepted: 01/15/2018] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Cross-sectional surveys of chimpanzee (Pan troglodytes) communities across sub-Saharan Africa show large geographical variation in malaria parasite (Plasmodium spp.) prevalence. The drivers leading to this apparent spatial heterogeneity may also be temporally dynamic but data on prevalence variation over time are missing for wild great apes. This study aims to fill this fundamental gap. METHODS Some 681 faecal samples were collected from 48 individuals of a group of habituated chimpanzees (Taï National Park, Côte d'Ivoire) across four non-consecutive sampling periods between 2005 and 2015. RESULTS Overall, 89 samples (13%) were PCR-positive for malaria parasite DNA. The proportion of positive samples ranged from 0 to 43% per month and 4 to 27% per sampling period. Generalized Linear Mixed Models detected significant seasonal and inter-annual variation, with seasonal increases during the wet seasons and apparently stochastic inter-annual variation. Younger individuals were also significantly more likely to test positive. CONCLUSIONS These results highlight strong temporal fluctuations of malaria parasite detection rates in wild chimpanzees. They suggest that the identification of other drivers of malaria parasite prevalence will require longitudinal approaches and caution against purely cross-sectional studies, which may oversimplify the dynamics of this host-parasite system.
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Affiliation(s)
- Doris F. Wu
- Project Group Epidemiology of Highly Pathogenic Microorganisms, Robert Koch-Institut, Seestraße 10, 13353 Berlin, Germany
- Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany
| | - Therese Löhrich
- Project Group Epidemiology of Highly Pathogenic Microorganisms, Robert Koch-Institut, Seestraße 10, 13353 Berlin, Germany
- Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany
| | - Andreas Sachse
- Project Group Epidemiology of Highly Pathogenic Microorganisms, Robert Koch-Institut, Seestraße 10, 13353 Berlin, Germany
| | - Roger Mundry
- Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany
| | - Roman M. Wittig
- Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany
- Taï Chimpanzee Project, Centre Suisse de Recherches Scientifiques, BP 1303, Abidjan 01, Côte d’Ivoire
| | - Sébastien Calvignac-Spencer
- Project Group Epidemiology of Highly Pathogenic Microorganisms, Robert Koch-Institut, Seestraße 10, 13353 Berlin, Germany
| | - Tobias Deschner
- Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany
| | - Fabian H. Leendertz
- Project Group Epidemiology of Highly Pathogenic Microorganisms, Robert Koch-Institut, Seestraße 10, 13353 Berlin, Germany
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Omondi CJ, Onguru D, Kamau L, Nanyingi M, Ong'amo G, Estambale B. Perennial transmission of malaria in the low altitude areas of Baringo County, Kenya. Malar J 2017. [PMID: 28623926 PMCID: PMC5474045 DOI: 10.1186/s12936-017-1904-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Malaria causes the greatest public health burden in sub-Saharan Africa where high mortality occurs mainly in children under 5 years of age. Traditionally, malaria has been reported mainly in the lowlands endemic regions of western Kenya, while the highlands of the Rift Valley have been relatively free except for the sporadic epidemics in some areas. Baringo County is located in the Kenyan highlands. The county generally experiences seasonal transmission of malaria. A few hotspots which experience continuous malaria transmission in the county do however exist. The objective of this study was to assess malaria infection status and identify areas with continuous transmissions with a view to mapping out probable transmission hot spots useful in mounting focused interventions within the county. METHODS Systematic sampling was employed to identify 1668 primary school pupils from fifteen primary schools located in 4 ecological zones (lowland, midland, highland and riverine) of three sub-counties of Baringo. Finger prick blood sampling was done every 4 months (during the dry season in February/March, after the long rains in June/July and short rains in November 2015). Malaria occurrence was tested using rapid diagnostic test kit (CareStart HRP-2 Pf). Microscopic examination was done on all RDT positive and 10% of negative cases. RESULTS A total of 268 (16.1%), out of 1668 pupils tested positive for Plasmodium falciparum by RDT; 78% had a single episode, 16.8% had 2 episodes, 4.9% had 3 episodes and 0.4% had 4 episodes. The riverine zone had the highest malaria cases (23.2%) followed by lowlands (0.9%). No malaria cases were detected in the midland zone while highland zone recorded only few cases during the third follow up. Up to 10.7% of malaria cases were reported in the dry season, 2.9% during the long rains and 5.7% in short rains season. CONCLUSIONS Malaria infection was prevalent in Baringo County and was mainly restricted to the riverine zone where transmission is continuous throughout the year. High malaria prevalence occurred in the dry season compared to the wet season. Even though malaria transmission is relatively low compared to endemic regions of Kenya, there is a need for continued monitoring of transmission dynamics under changing climatic conditions as well as establishing expanded malaria control strategies especially within the riverine zone which would include an integrated mosquito control and chemotherapy for infected individuals.
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Affiliation(s)
- Collince J Omondi
- Department of Zoological Sciences, Kenyatta University, P.O. Box 43844, Nairobi, 00100, Kenya
| | - Daniel Onguru
- School of Health Sciences, Jaramogi Oginga Odinga University of Science and Technology, P.O. Box 210, Bondo, 40601, Kenya.
| | - Lucy Kamau
- Department of Zoological Sciences, Kenyatta University, P.O. Box 43844, Nairobi, 00100, Kenya
| | - Mark Nanyingi
- Department of Public Health, Pharmacology and Toxicology, University of Nairobi, P.O. Box 30197, Nairobi, 00100, Kenya
| | - George Ong'amo
- School of Biological Sciences, University of Nairobi, P.O. Box 30197, Nairobi, 00100, Kenya
| | - Benson Estambale
- School of Health Sciences, Jaramogi Oginga Odinga University of Science and Technology, P.O. Box 210, Bondo, 40601, Kenya
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Chuang TW, Soble A, Ntshalintshali N, Mkhonta N, Seyama E, Mthethwa S, Pindolia D, Kunene S. Assessment of climate-driven variations in malaria incidence in Swaziland: toward malaria elimination. Malar J 2017; 16:232. [PMID: 28571572 PMCID: PMC5455096 DOI: 10.1186/s12936-017-1874-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Accepted: 05/24/2017] [Indexed: 12/01/2022] Open
Abstract
Background Swaziland aims to eliminate malaria by 2020. However, imported cases from neighbouring endemic countries continue to sustain local parasite reservoirs and initiate transmission. As certain weather and climatic conditions may trigger or intensify malaria outbreaks, identification of areas prone to these conditions may aid decision-makers in deploying targeted malaria interventions more effectively. Methods Malaria case-surveillance data for Swaziland were provided by Swaziland’s National Malaria Control Programme. Climate data were derived from local weather stations and remote sensing images. Climate parameters and malaria cases between 2001 and 2015 were then analysed using seasonal autoregressive integrated moving average models and distributed lag non-linear models (DLNM). Results The incidence of malaria in Swaziland increased between 2005 and 2010, especially in the Lubombo and Hhohho regions. A time-series analysis indicated that warmer temperatures and higher precipitation in the Lubombo and Hhohho administrative regions are conducive to malaria transmission. DLNM showed that the risk of malaria increased in Lubombo when the maximum temperature was above 30 °C or monthly precipitation was above 5 in. In Hhohho, the minimum temperature remaining above 15 °C or precipitation being greater than 10 in. might be associated with malaria transmission. Conclusions This study provides a preliminary assessment of the impact of short-term climate variations on malaria transmission in Swaziland. The geographic separation of imported and locally acquired malaria, as well as population behaviour, highlight the varying modes of transmission, part of which may be relevant to climate conditions. Thus, the impact of changing climate conditions should be noted as Swaziland moves toward malaria elimination. Electronic supplementary material The online version of this article (doi:10.1186/s12936-017-1874-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ting-Wu Chuang
- Department of Molecular Parasitology and Tropical Diseases, School of Medicine, College of Medicine, Taipei Medical University, No. 250, Wuxing St. Sinyi District, Taipei, 100, Taiwan.
| | - Adam Soble
- Clinton Health Access Initiative, Manzini, Swaziland
| | | | - Nomcebo Mkhonta
- National Malaria Control Programme, Ministry of Health, Manzini, Swaziland
| | - Eric Seyama
- Swaziland Meteorological Service, Mbabane, Swaziland
| | - Steven Mthethwa
- National Malaria Control Programme, Ministry of Health, Manzini, Swaziland
| | | | - Simon Kunene
- National Malaria Control Programme, Ministry of Health, Manzini, Swaziland
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36
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Kipruto EK, Ochieng AO, Anyona DN, Mbalanya M, Mutua EN, Onguru D, Nyamongo IK, Estambale BBA. Effect of climatic variability on malaria trends in Baringo County, Kenya. Malar J 2017; 16:220. [PMID: 28545590 PMCID: PMC5445289 DOI: 10.1186/s12936-017-1848-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Accepted: 05/02/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Malaria transmission in arid and semi-arid regions of Kenya such as Baringo County, is seasonal and often influenced by climatic factors. Unravelling the relationship between climate variables and malaria transmission dynamics is therefore instrumental in developing effective malaria control strategies. The main aim of this study was to describe the effects of variability of rainfall, maximum temperature and vegetation indices on seasonal trends of malaria in selected health facilities within Baringo County, Kenya. METHODS Climate variables sourced from the International Research Institute (IRI)/Lamont-Doherty Earth Observatory (LDEO) climate database and malaria cases reported in 10 health facilities spread across four ecological zones (riverine, lowland, mid-altitude and highland) between 2004 and 2014 were subjected to a time series analysis. A negative binomial regression model with lagged climate variables was used to model long-term monthly malaria cases. The seasonal Mann-Kendall trend test was then used to detect overall monotonic trends in malaria cases. RESULTS Malaria cases increased significantly in the highland and midland zones over the study period. Changes in malaria prevalence corresponded to variations in rainfall and maximum temperature. Rainfall at a time lag of 2 months resulted in an increase in malaria transmission across the four zones while an increase in temperature at time lags of 0 and 1 month resulted in an increase in malaria cases in the riverine and highland zones, respectively. CONCLUSION Given the existence of a time lag between climatic variables more so rainfall and peak malaria transmission, appropriate control measures can be initiated at the onset of short and after long rains seasons.
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Affiliation(s)
- Edwin K Kipruto
- Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium.,Division of Research Innovation and Outreach, Jaramogi Oginga Odinga University of Science and Technology, P. O. Box 210, Bondo, 40601, Kenya
| | - Alfred O Ochieng
- School of Biological and Physical Sciences, Jaramogi Oginga Odinga University of Science and Technology, P. O. Box 210, Bondo, 40601, Kenya
| | - Douglas N Anyona
- Division of Research Innovation and Outreach, Jaramogi Oginga Odinga University of Science and Technology, P. O. Box 210, Bondo, 40601, Kenya
| | - Macrae Mbalanya
- Division of Research Innovation and Outreach, Jaramogi Oginga Odinga University of Science and Technology, P. O. Box 210, Bondo, 40601, Kenya
| | - Edna N Mutua
- Institute of Anthropology, Gender and African Studies, University of Nairobi, P.O. Box 30197, Nairobi, 00100, Kenya
| | - Daniel Onguru
- School of Health Sciences, Jaramogi Oginga Odinga University of Science and Technology, P. O. Box 210, Bondo, 40601, Kenya
| | - Isaac K Nyamongo
- Institute of Anthropology, Gender and African Studies, University of Nairobi, P.O. Box 30197, Nairobi, 00100, Kenya
| | - Benson B A Estambale
- Division of Research Innovation and Outreach, Jaramogi Oginga Odinga University of Science and Technology, P. O. Box 210, Bondo, 40601, Kenya.
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Abiodun GJ, Witbooi P, Okosun KO. Modeling and analyzing the impact of temperature and rainfall on mosquito population dynamics over Kwazulu-Natal, South Africa. INT J BIOMATH 2017. [DOI: 10.1142/s1793524517500553] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Malaria parasites are strongly dependent on Anopheles mosquitoes for transmission; for this reason, mosquito population dynamics are a crucial determinant of malaria risk. However, temperature and rainfall play a significant role in both aquatic and adult stages of the Anopheles. Consequently, it is important to understand the biology of malaria vector mosquitoes in the study of malaria transmission. In this study, we develop a climate-based, ordinary-differential-equation model to analyze how rainfall and temperature determine mosquito population size. In the model, we consider in detail the influence of ambient temperature on gonotrophic and sporogonic cycles over Amajuba District, Kwazulu-Natal Province, South Africa. In particular, we further use the model to simulate the spatial distribution of the mosquito biting rate over the study region. Our results reflect high seasonality of the population of An. gambiae over the region and also demonstrate the influence of climatic factors on the mosquito population dynamics.
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Affiliation(s)
- Gbenga J. Abiodun
- Department of Mathematics and Applied Mathematics, University of the Western Cape, Private Bag X17, Bellville 7535, Republic of South Africa
| | - Peter Witbooi
- Department of Mathematics and Applied Mathematics, University of the Western Cape, Private Bag X17, Bellville 7535, Republic of South Africa
| | - Kazeem O. Okosun
- Department of Mathematics, Vaal University of Technology, Private Bag X021, Vanderbijlpark, Andries Potgieter Blvrd-1900, Republic of South Africa
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Cohen JM, Venesky MD, Sauer EL, Civitello DJ, McMahon TA, Roznik EA, Rohr JR. The thermal mismatch hypothesis explains host susceptibility to an emerging infectious disease. Ecol Lett 2017; 20:184-193. [DOI: 10.1111/ele.12720] [Citation(s) in RCA: 119] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Revised: 10/06/2016] [Accepted: 11/17/2016] [Indexed: 01/15/2023]
Affiliation(s)
- Jeremy M. Cohen
- Department of Integrative Biology University of South Florida Tampa FL USA
| | | | - Erin L. Sauer
- Department of Integrative Biology University of South Florida Tampa FL USA
| | - David J. Civitello
- Department of Integrative Biology University of South Florida Tampa FL USA
| | | | | | - Jason R. Rohr
- Department of Integrative Biology University of South Florida Tampa FL USA
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Anwar MY, Lewnard JA, Parikh S, Pitzer VE. Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence. Malar J 2016; 15:566. [PMID: 27876041 PMCID: PMC5120433 DOI: 10.1186/s12936-016-1602-1] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Accepted: 11/04/2016] [Indexed: 01/09/2023] Open
Abstract
Background Malaria remains endemic in Afghanistan. National control and prevention strategies would be greatly enhanced through a better ability to forecast future trends in disease incidence. It is, therefore, of interest to develop a predictive tool for malaria patterns based on the current passive and affordable surveillance system in this resource-limited region. Methods This study employs data from Ministry of Public Health monthly reports from January 2005 to September 2015. Malaria incidence in Afghanistan was forecasted using autoregressive integrated moving average (ARIMA) models in order to build a predictive tool for malaria surveillance. Environmental and climate data were incorporated to assess whether they improve predictive power of models. Results Two models were identified, each appropriate for different time horizons. For near-term forecasts, malaria incidence can be predicted based on the number of cases in the four previous months and 12 months prior (Model 1); for longer-term prediction, malaria incidence can be predicted using the rates 1 and 12 months prior (Model 2). Next, climate and environmental variables were incorporated to assess whether the predictive power of proposed models could be improved. Enhanced vegetation index was found to have increased the predictive accuracy of longer-term forecasts. Conclusion Results indicate ARIMA models can be applied to forecast malaria patterns in Afghanistan, complementing current surveillance systems. The models provide a means to better understand malaria dynamics in a resource-limited context with minimal data input, yielding forecasts that can be used for public health planning at the national level. Electronic supplementary material The online version of this article (doi:10.1186/s12936-016-1602-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mohammad Y Anwar
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | - Joseph A Lewnard
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Sunil Parikh
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Virginia E Pitzer
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
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Onyango EA, Sahin O, Awiti A, Chu C, Mackey B. An integrated risk and vulnerability assessment framework for climate change and malaria transmission in East Africa. Malar J 2016; 15:551. [PMID: 27835976 PMCID: PMC5105305 DOI: 10.1186/s12936-016-1600-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Accepted: 11/04/2016] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Malaria is one of the key research concerns in climate change-health relationships. Numerous risk assessments and modelling studies provide evidence that the transmission range of malaria will expand with rising temperatures, adversely impacting on vulnerable communities in the East African highlands. While there exist multiple lines of evidence for the influence of climate change on malaria transmission, there is insufficient understanding of the complex and interdependent factors that determine the risk and vulnerability of human populations at the community level. Moreover, existing studies have had limited focus on the nature of the impacts on vulnerable communities or how well they are prepared to cope. In order to address these gaps, a systems approach was used to present an integrated risk and vulnerability assessment framework for studies of community level risk and vulnerability to malaria due to climate change. RESULTS Drawing upon published literature on existing frameworks, a systems approach was applied to characterize the factors influencing the interactions between climate change and malaria transmission. This involved structural analysis to determine influential, relay, dependent and autonomous variables in order to construct a detailed causal loop conceptual model that illustrates the relationships among key variables. An integrated assessment framework that considers indicators of both biophysical and social vulnerability was proposed based on the conceptual model. CONCLUSIONS A major conclusion was that this integrated assessment framework can be implemented using Bayesian Belief Networks, and applied at a community level using both quantitative and qualitative methods with stakeholder engagement. The approach enables a robust assessment of community level risk and vulnerability to malaria, along with contextually relevant and targeted adaptation strategies for dealing with malaria transmission that incorporate both scientific and community perspectives.
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Affiliation(s)
- Esther Achieng Onyango
- Centre for Environment and Population Health, Griffith University, School of Environment, 170 Kessels Road, Nathan, 4111 Australia
| | - Oz Sahin
- School of Engineering, Griffith University, Gold Coast, 4222 Australia
- Griffith Climate Change Response Program, Griffith University, Gold Coast, 4222 Australia
| | - Alex Awiti
- East African Institute, Aga Khan University East Africa, 2nd Parklands Avenue, Nairobi, 00100 Kenya
| | - Cordia Chu
- Centre for Environment and Population Health, Griffith University, School of Environment, 170 Kessels Road, Nathan, 4111 Australia
| | - Brendan Mackey
- Griffith Climate Change Response Program, Griffith University, Gold Coast, 4222 Australia
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Boyce R, Reyes R, Matte M, Ntaro M, Mulogo E, Metlay JP, Band L, Siedner MJ. Severe Flooding and Malaria Transmission in the Western Ugandan Highlands: Implications for Disease Control in an Era of Global Climate Change. J Infect Dis 2016; 214:1403-1410. [PMID: 27534686 DOI: 10.1093/infdis/jiw363] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2016] [Accepted: 06/28/2016] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND There are several mechanisms by which global climate change may impact malaria transmission. We sought to assess how the increased frequency of extreme precipitation events associated with global climate change will influence malaria transmission in highland areas of East Africa. METHODS We used a differences-in-differences, quasi-experimental design to examine spatial variability in the incidence rate of laboratory-confirmed malaria cases and malaria-related hospitalizations between villages (1) at high versus low elevations, (2) with versus without rivers, and (3) upstream versus downstream before and after severe flooding that occurred in Kasese District, Western Region, Uganda, in May 2013. RESULTS During the study period, 7596 diagnostic tests were performed, and 1285 patients were admitted with a diagnosis of malaria. We observed that extreme flooding resulted in an increase of approximately 30% in the risk of an individual having a positive result of a malaria diagnostic test in the postflood period in villages bordering a flood-affected river, compared with villages farther from a river, with a larger relative impact on upstream versus downstream villages (adjusted rate ratio, 1.91 vs 1.33). CONCLUSIONS Extreme precipitation such as the flooding described here may pose significant challenges to malaria control programs and will demand timely responses to mitigate deleterious impacts on human health.
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Affiliation(s)
| | - Raquel Reyes
- Division of General Medicine and Clinical Epidemiology, University of North Carolina School of Medicine
| | - Michael Matte
- Department of Community Health, Mbarara University of Science and Technology, Uganda
| | - Moses Ntaro
- Department of Community Health, Mbarara University of Science and Technology, Uganda
| | - Edgar Mulogo
- Department of Community Health, Mbarara University of Science and Technology, Uganda
| | - Joshua P Metlay
- Division of General Internal Medicine, Massachusetts General Hospital
| | - Lawrence Band
- Department of Geography, University of North Carolina, Chapel Hill
| | - Mark J Siedner
- Department of Medicine, Harvard Medical School and Massachusetts General Hospital, Boston
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Seasonal and Geographic Variation of Pediatric Malaria in Burundi: 2011 to 2012. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2016; 13:425. [PMID: 27092518 PMCID: PMC4847087 DOI: 10.3390/ijerph13040425] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2016] [Revised: 04/06/2016] [Accepted: 04/11/2016] [Indexed: 01/06/2023]
Abstract
We analyzed hospitalization records from 2011 to 2012 to examine the spatial patterns of pediatric malaria in Burundi. Malaria case data for those below the age of five years were categorized according to the four principal seasons of Burundi, which are two rainy seasons (February to May; September to November) and two dry seasons (June to August; December to January). The Getis-Ord Gi* statistic was used to examine seasonal spatial patterns of pediatric malaria, whereas geographically weighted regression (GWR) were used to examine the potential role of environmental variables on the spatial patterns of cases. There were a total of 19,890 pediatric malaria cases reported during the study period. The incidence among males was higher than that among females; and it was higher in rural districts. The seasonal incidence peaks occurred in the northern half of the country during the wet season while during the dry season, incidence was higher in southern Burundi. Elevation played a greater role in explaining variance in the prevalence of pediatric malaria during seasonal peaks than rainfall. The counterintuitive finding in northern Burundi confirms previous findings and suggests other factors (e.g., land cover/land use) facilitate the persistence of the mosquito population in the highlands of Africa.
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Sena L, Deressa W, Ali A. Correlation of Climate Variability and Malaria: A Retrospective Comparative Study, Southwest Ethiopia. Ethiop J Health Sci 2016; 25:129-38. [PMID: 26124620 PMCID: PMC4478264 DOI: 10.4314/ejhs.v25i2.5] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Background Climatic variables can determine malaria transmission dynamics. To see the correlation between malaria occurrence and climatic variables, records of malaria episodes over eight years period were analyzed incorporating climatic variables around Gilgel-Gibe Hydroelectric Dam and control sites. Methods Records of 99,206 confirmed malaria episodes registered between 2003 and 2011 were analyzed along with local meteorological data of the same duration. Data were analyzed with SPSS statistical software version 20 for Windows. Spearman correlation coefficient was estimated as a measure of the correlation. Results The major peaks of malaria prevalence were observed following the peaks of rainfall in the Gilgel-Gibe Hydroelectric Dam site. In the control site, the peaks of malaria in some years coincided with the peaks of rainfall, and the pattern of rainfall was relatively less fluctuating. Mean rainfall was negatively correlated with number of malaria cases at lags of 0 and 1 month, but positively correlated at lags of 2 to 4 months. Mean relative humidity showed significant positive correlations at lags of 3 to 4 months. Monthly mean maximum and minimum temperatures weakly correlated at lags of 0 to 4 months. Conclusions Correlations of malaria and climate variables were different for the two sites; in Gilgel-Gibe, rainfall and relative humidity showed positive correlations. However, in the control site, the correlation of weather variables and malaria episodes were insignificant. Exploration of additional factors such as vegetation index and physico-chemical nature of mosquito breeding site may improve understanding of determinants of malaria dynamics in the area.
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Affiliation(s)
- Lelisa Sena
- Department of Epidemiology, College of Public Health and Medical Sciences, Jimma University, Ethiopia ; Department of Preventive Medicine, School of Public Health, College of Health Sciences, Addis Ababa University, Ethiopia
| | - Wakgari Deressa
- Department of Preventive Medicine, School of Public Health, College of Health Sciences, Addis Ababa University, Ethiopia
| | - Ahmed Ali
- Department of Preventive Medicine, School of Public Health, College of Health Sciences, Addis Ababa University, Ethiopia
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Chirebvu E, Chimbari MJ, Ngwenya BN, Sartorius B. Clinical Malaria Transmission Trends and Its Association with Climatic Variables in Tubu Village, Botswana: A Retrospective Analysis. PLoS One 2016; 11:e0139843. [PMID: 26983035 PMCID: PMC4794139 DOI: 10.1371/journal.pone.0139843] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2015] [Accepted: 09/16/2015] [Indexed: 11/21/2022] Open
Abstract
Good knowledge on the interactions between climatic variables and malaria can be very useful for predicting outbreaks and preparedness interventions. We investigated clinical malaria transmission patterns and its temporal relationship with climatic variables in Tubu village, Botswana. A 5-year retrospective time series data analysis was conducted to determine the transmission patterns of clinical malaria cases at Tubu Health Post and its relationship with rainfall, flood discharge, flood extent, mean minimum, maximum and average temperatures. Data was obtained from clinical records and respective institutions for the period July 2005 to June 2010, presented graphically and analysed using the Univariate ANOVA and Pearson cross-correlation coefficient tests. Peak malaria season occurred between October and May with the highest cumulative incidence of clinical malaria cases being recorded in February. Most of the cases were individuals aged >5 years. Associations between the incidence of clinical malaria cases and several factors were strong at lag periods of 1 month; rainfall (r = 0.417), mean minimum temperature (r = 0.537), mean average temperature (r = 0.493); and at lag period of 6 months for flood extent (r = 0.467) and zero month for flood discharge (r = 0.497). The effect of mean maximum temperature was strongest at 2-month lag period (r = 0.328). Although malaria transmission patterns varied from year to year the trends were similar to those observed in sub-Saharan Africa. Age group >5 years experienced the greatest burden of clinical malaria probably due to the effects of the national malaria elimination programme. Rainfall, flood discharge and extent, mean minimum and mean average temperatures showed some correlation with the incidence of clinical malaria cases.
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Affiliation(s)
- Elijah Chirebvu
- Okavango Research Institute, University of Botswana, Private Bag 285, Maun, Botswana
| | - Moses John Chimbari
- College of Health Sciences, Howard Campus, University of KwaZulu-Natal, Durban 4041, South Africa
| | | | - Benn Sartorius
- Discipline of Public Health Medicine, School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa
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Karuri SW, Snow RW. Forecasting paediatric malaria admissions on the Kenya Coast using rainfall. Glob Health Action 2016; 9:29876. [PMID: 26842613 PMCID: PMC4740093 DOI: 10.3402/gha.v9.29876] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Revised: 11/29/2015] [Accepted: 12/29/2015] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Malaria is a vector-borne disease which, despite recent scaled-up efforts to achieve control in Africa, continues to pose a major threat to child survival. The disease is caused by the protozoan parasite Plasmodium and requires mosquitoes and humans for transmission. Rainfall is a major factor in seasonal and secular patterns of malaria transmission along the East African coast. OBJECTIVE The goal of the study was to develop a model to reliably forecast incidences of paediatric malaria admissions to Kilifi District Hospital (KDH). DESIGN In this article, we apply several statistical models to look at the temporal association between monthly paediatric malaria hospital admissions, rainfall, and Indian Ocean sea surface temperatures. Trend and seasonally adjusted, marginal and multivariate, time-series models for hospital admissions were applied to a unique data set to examine the role of climate, seasonality, and long-term anomalies in predicting malaria hospital admission rates and whether these might become more or less predictable with increasing vector control. RESULTS The proportion of paediatric admissions to KDH that have malaria as a cause of admission can be forecast by a model which depends on the proportion of malaria admissions in the previous 2 months. This model is improved by incorporating either the previous month's Indian Ocean Dipole information or the previous 2 months' rainfall. CONCLUSIONS Surveillance data can help build time-series prediction models which can be used to anticipate seasonal variations in clinical burdens of malaria in stable transmission areas and aid the timing of malaria vector control.
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Affiliation(s)
- Stella Wanjugu Karuri
- Spatial Health Metrics Group, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya;
| | - Robert W Snow
- Spatial Health Metrics Group, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya.,Nuffield Department of Clinical Medicine, Centre for Tropical Medicine & Global Health, University of Oxford, Oxford, United Kingdom
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Ostovar A, Haghdoost AA, Rahimiforoushani A, Raeisi A, Majdzadeh R. Time Series Analysis of Meteorological Factors Influencing Malaria in South Eastern Iran. J Arthropod Borne Dis 2016; 10:222-36. [PMID: 27308280 PMCID: PMC4906761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2013] [Accepted: 02/21/2015] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND The Malaria Early Warning System is defined as the use of prognostic variables for predicting the occurrence of malaria epidemics several months in advance. The principal objective of this study was to provide a malaria prediction model by using meteorological variables and historical malaria morbidity data for malaria-endemic areas in south eastern Iran. METHODS A total of 2002 locally transmitted microscopically confirmed malaria cases, which occurred in the Minab district of Hormozgan Province in Iran over a period of 6 years from March 2003 to March 2009, were analysed. Meteorological variables (the rainfall, temperature, and relative humidity in this district) were also assessed. Monthly and weekly autocorrelation functions, partial autocorrelation functions, and cross-correlation graphs were examined to explore the relationship between the historical morbidity data and meteorological variables and the number of cases of malaria. Having used univariate auto-regressive integrated moving average or transfer function models, significant predictors among the meteorological variables were selected to predict the number of monthly and weekly malaria cases. Ljung-Box statistics and stationary R-squared were used for model diagnosis and model fit, respectively. RESULTS The weekly model had a better fit (R(2)= 0.863) than the monthly model (R(2)= 0.424). However, the Ljung-Box statistic was significant for the weekly model. In addition to autocorrelations, meteorological variables were not significant, except for different orders of maximum and minimum temperatures in the monthly model. CONCLUSIONS Time-series models can be used to predict malaria incidence with acceptable accuracy in a malaria early-warning system. The applicability of using routine meteorological data in statistical models is seriously limited.
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Affiliation(s)
- Afshin Ostovar
- Epidemiology and Biostatistics Department, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Akbar Haghdoost
- Research Center for Modeling in Health, Institute for Future Studies in Health, Kerman University of Medical Sciences, Kerman, Iran,Corresponding author: Dr Ali Akbar Haghdoost,
| | - Abbas Rahimiforoushani
- Epidemiology and Biostatistics Department, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Ahmad Raeisi
- Malaria Control Office of MOH and ME, Tehran University of Medical Sciences, Tehran, Iran
| | - Reza Majdzadeh
- Knowledge Utilization Research Center, Tehran University of Medical Sciences, Tehran, Iran
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Bicout DJ, Vautrin M, Vignolles C, Sabatier P. Modeling the dynamics of mosquito breeding sites vs rainfall in Barkedji area, Senegal. Ecol Modell 2015. [DOI: 10.1016/j.ecolmodel.2015.08.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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48
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Roy M, Bouma M, Dhiman RC, Pascual M. Predictability of epidemic malaria under non-stationary conditions with process-based models combining epidemiological updates and climate variability. Malar J 2015; 14:419. [PMID: 26502881 PMCID: PMC4623260 DOI: 10.1186/s12936-015-0937-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2015] [Accepted: 10/09/2015] [Indexed: 11/10/2022] Open
Abstract
Background Previous studies have demonstrated the feasibility of early-warning systems for epidemic malaria informed by climate variability. Whereas modelling approaches typically assume stationary conditions, epidemiological systems are characterized by changes in intervention measures over time, at scales typically longer than inter-epidemic periods. These trends in control efforts preclude simple application of early-warning systems validated by retrospective surveillance data; their effects are also difficult to distinguish from those of climate variability itself. Methods Rainfall-driven transmission models for falciparum and vivax malaria are fitted to long-term retrospective surveillance data from four districts in northwest India. Maximum-likelihood estimates (MLEs) of model parameters are obtained for each district via a recently introduced iterated filtering method for partially observed Markov processes. The resulting MLE model is then used to generate simulated yearly forecasts in two different ways, and these forecasts are compared with more recent (out-of-fit) data. In the first approach, initial conditions for generating the predictions are repeatedly updated on a yearly basis, based on the new epidemiological data and the inference method that naturally lends itself to this purpose, given its time-sequential application. In the second approach, the transmission parameters themselves are also updated by refitting the model over a moving window of time. Results Application of these two approaches to examine the predictability of epidemic malaria in the different districts reveals differences in the effectiveness of intervention for the two parasites, and illustrates how the ‘failure’ of predictions can be informative to evaluate and quantify the effect of control efforts in the context of climate variability. The first approach performs adequately, and sometimes even better than the second one, when the climate remains the major driver of malaria dynamics, as found for Plasmodium vivax for which an effective clinical intervention is lacking. The second approach offers more skillful forecasts when the dynamics shift over time, as is the case of Plasmodium falciparum in recent years with declining incidence under improved control. Conclusions Predictive systems for infectious diseases such as malaria, based on process-based models and climate variables, can be informative and applicable under non-stationary conditions. Electronic supplementary material The online version of this article (doi:10.1186/s12936-015-0937-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Manojit Roy
- Howard Hughes Medical Institute, University of Michigan, Ann Arbor, 48109, MI, USA. .,Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA.
| | - Menno Bouma
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK. .,Climate Dynamics and Impacts Unit, Institut Catala de Sciencies del Clima, 08005, Barcelona, Catalonia, Spain.
| | - Ramesh C Dhiman
- National Institute of Malaria Research (ICMR), Delhi, India.
| | - Mercedes Pascual
- Howard Hughes Medical Institute, University of Michigan, Ann Arbor, 48109, MI, USA. .,Department of Ecology and Evolution, University of Chicago, 1101 E 57th Street, Chicago, IL, 60637, USA.
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Oluwole OSA. Climate Regimes, El Niño-Southern Oscillation, and Meningococcal Meningitis Epidemics. Front Public Health 2015; 3:187. [PMID: 26284234 PMCID: PMC4519658 DOI: 10.3389/fpubh.2015.00187] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2015] [Accepted: 07/14/2015] [Indexed: 11/20/2022] Open
Abstract
Meningococcal meningitis is a major public health problem that kills thousands annually in Africa, Europe, North, and South America. Occurrence is, however, highest during the dry seasons in Sahel Africa. Interannual changes in precipitation correlate with interannual changes in El Niño-Southern Oscillation (ENSO), while interdecadal changes in precipitation correlate with Pacific Decadal Oscillation (PDO). The objective of the study was to determine if there is spectral coherence of seasonal, interannual, and interdecadal changes in occurrence of meningococcal meningitis in Sahel, Central, and East Africa with interannual and interdecadal changes of PDO and ENSO. Time series were fitted to occurrence of meningococcal meningitis in Sahel, Central, and East Africa, to indices of precipitation anomalies in the Sahel, and to indices of ENSO and PDO anomalies. Morlet wavelet was used to transform the time series to frequency-time domain. Wavelet spectra and coherence analyses were performed. Occurrence of meningococcal meningitis showed seasonal, interannual, and interdecadal changes. The magnitude of occurrence was higher during warm climate regime, and strong El Niños. Spectra coherence of interannual and interdecadal changes of ENSO and PDO with occurrence of meningococcal meningitis in Sahel, Central, and East Africa were significant at p < 0.0001. Precipitation in Sahel was low during warm climate regimes. Spectra coherence of changes in precipitation in Sahel with ENSO was significant at p < 0.0001. ENSO and PDO are determinants of the seasonal, interannual, and interdecadal changes in occurrence of meningococcal meningitis. Public health management of epidemics of meningococcal meningitis should include forecast models of changes in ENSO to predict periods of low precipitation, which initiate occurrence.
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Midekisa A, Beyene B, Mihretie A, Bayabil E, Wimberly MC. Seasonal associations of climatic drivers and malaria in the highlands of Ethiopia. Parasit Vectors 2015; 8:339. [PMID: 26104276 PMCID: PMC4488986 DOI: 10.1186/s13071-015-0954-7] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2014] [Accepted: 06/13/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The impacts of interannual climate fluctuations on vector-borne diseases, especially malaria, have received considerable attention in the scientific literature. These effects can be significant in semi-arid and high-elevation areas such as the highlands of East Africa because cooler temperature and seasonally dry conditions limit malaria transmission. Many previous studies have examined short-term lagged effects of climate on malaria (weeks to months), but fewer have explored the possibility of longer-term seasonal effects. METHODS This study assessed the interannual variability of malaria occurrence from 2001 to 2009 in the Amhara region of Ethiopia. We tested for associations of climate variables summarized during the dry (January-April), early transition (May-June), and wet (July-September) seasons with malaria incidence in the early peak (May-July) and late peak (September-December) epidemic seasons using generalized linear models. Climate variables included land surface temperature (LST), rainfall, actual evapotranspiration (ET), and the enhanced vegetation index (EVI). RESULTS We found that both early and late peak malaria incidence had the strongest associations with meteorological conditions in the preceding dry and early transition seasons. Temperature had the strongest influence in the wetter western districts, whereas moisture variables had the strongest influence in the drier eastern districts. We also found a significant correlation between malaria incidence in the early and the subsquent late peak malaria seasons, and the addition of early peak malaria incidence as a predictor substantially improved models of late peak season malaria in both of the study sub-regions. CONCLUSIONS These findings suggest that climatic effects on malaria prior to the main rainy season can carry over through the rainy season and affect the probability of malaria epidemics during the late malaria peak. The results also emphasize the value of combining environmental monitoring with epidemiological surveillance to develop forecasts of malaria outbreaks, as well as the need for spatially stratified approaches that reflect the differential effects of climatic variations in the different sub-regions.
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Affiliation(s)
- Alemayehu Midekisa
- Geospatial Sciences Center of Excellence (GSCE), South Dakota State University, Brookings, SD, USA
| | - Belay Beyene
- Amhara Regional Health Bureau, Bahir Dar, Ethiopia
| | - Abere Mihretie
- Health Development and Anti-Malaria Association, Addis Ababa, Ethiopia
| | - Estifanos Bayabil
- Health Development and Anti-Malaria Association, Addis Ababa, Ethiopia
| | - Michael C Wimberly
- Geospatial Sciences Center of Excellence (GSCE), South Dakota State University, Brookings, SD, USA.
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