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Navas ALA, Janko MM, Benítez FL, Narvaez M, Vasco LE, Kansara P, Zaitchik B, Pan WK, Mena CF. Impact of climate and land use/land cover changes on malaria incidence in the Ecuadorian Amazon. PLOS CLIMATE 2024; 3:e0000315. [PMID: 39027120 PMCID: PMC11257155 DOI: 10.1371/journal.pclm.0000315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Malaria transmission is influenced by climate and land use/land cover change (LULC). This study examines the impact of climate and LULC on malaria risk in the Ecuadorian Amazon. Weekly malaria surveillance data between 2008 and 2019 from Ecuador's Ministry of Public Health were combined with hydrometeorological and LULC data. Cross-correlation analyses identified time lags. Bayesian spatiotemporal models estimated annual LULC rates of change (ARC) by census area and assessed the effects on Plasmodium vivax and Plasmodium falciparum incidence. ARC for the five land cover classes (forest, agriculture, urban, shrub vegetation, water) ranged from -1 to 4% with agriculture increasing across areas. Forest and shrub vegetation ARC were significantly associated with both Plasmodium vivax and Plasmodium falciparum. Temperature and terrestrial water content showed consistent negative relationships with both species. Precipitation had varying effects on Plasmodium vivax (null) and Plasmodium falciparum (increase) incidence. Shrubs and forest expansion, increased temperature, and terrestrial water content reduced malaria incidence, while increased precipitation had varying effects. Relationships between malaria, LULC, and climate are complex, influencing risk profiles. These findings aid decision-making and guide further research in the region.
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Affiliation(s)
- Andrea L. Araujo Navas
- Universidad San Francisco de Quito USFQ, Institute of Geography, Quito, Pichincha, Ecuador
| | - Mark M. Janko
- Duke Global Health Institute, Duke University, Durham, North Carolina, United States of America
| | - Fátima L. Benítez
- Universidad San Francisco de Quito USFQ, Institute of Geography, Quito, Pichincha, Ecuador
- Department of Earth and Environmental Sciences, Faculty of Bioscience Engineering, Katholiek Universiteit van Leuven, Leuven, Belgium
| | - Manuel Narvaez
- Universidad San Francisco de Quito USFQ, Institute of Geography, Quito, Pichincha, Ecuador
| | - Luis E. Vasco
- Universidad San Francisco de Quito USFQ, Institute of Geography, Quito, Pichincha, Ecuador
| | - Prakrut Kansara
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Benjamin Zaitchik
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - William K. Pan
- Duke Global Health Institute, Duke University, Durham, North Carolina, United States of America
| | - Carlos F. Mena
- Universidad San Francisco de Quito USFQ, Institute of Geography, Quito, Pichincha, Ecuador
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Kane F, Toure M, Sogoba N, Traore B, Keita M, Konate D, Diawara SI, Sanogo D, Keita S, Sanogo I, Doumbia CO, Keïta B, Traoré AS, Sissoko I, Coulibaly H, Thiam SM, Barry A, Shaffer JG, Diakite M, Doumbia S. Modeling clinical malaria episodes in different ecological settings in Mali, 2018-2022. IJID REGIONS 2024; 10:24-30. [PMID: 38076024 PMCID: PMC10698665 DOI: 10.1016/j.ijregi.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 11/06/2023] [Accepted: 11/08/2023] [Indexed: 02/12/2024]
Abstract
Objectives Following the scaling-up of malaria control strategies in Mali, understanding the changes in age-specific prevalence of infection and risk factors associated with remains necessary to determine new priorities to progress toward disease elimination. This study aimed to estimate the risk of clinical malaria using longitudinal data across three different transmission settings in Mali. Methods Cohort-based longitudinal studies were performed from April 2018 to December 2022. Incidence of malaria was measured through community health center-based passive case detection. Generalized estimation equation model was used to assess risk factors for clinical malaria. Results A total of 21,453 clinical presentations were reported from 4500 participants, mainly from July to November. Data shows a significant association between malaria episodes, sex, age group, season, and year. Women had lower risk, the risk of clinical episode increased with age up to 14 years then declined, and in both sites, the dry-season risk of clinical episode was significantly lower compared to the rainy season. Conclusion Determining factors associated with the occurrence of clinical malaria across different ecological settings across the country could help in the development of new strategies aiming to accelerate malaria elimination in an area where malaria transmission remains intense.
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Affiliation(s)
- Fousseyni Kane
- West African International Center for Excellence in Malaria Research, Techniques and Technologies of Bamako, Bamako, Mali
- University Clinical Research Center (UCRC), University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Mahamoudou Toure
- West African International Center for Excellence in Malaria Research, Techniques and Technologies of Bamako, Bamako, Mali
- University Clinical Research Center (UCRC), University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Nafomon Sogoba
- West African International Center for Excellence in Malaria Research, Techniques and Technologies of Bamako, Bamako, Mali
- Malaria Research and Training Center (MRTC), University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Bourama Traore
- West African International Center for Excellence in Malaria Research, Techniques and Technologies of Bamako, Bamako, Mali
- Malaria Research and Training Center (MRTC), University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Moussa Keita
- West African International Center for Excellence in Malaria Research, Techniques and Technologies of Bamako, Bamako, Mali
- Malaria Research and Training Center (MRTC), University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Drissa Konate
- West African International Center for Excellence in Malaria Research, Techniques and Technologies of Bamako, Bamako, Mali
- University Clinical Research Center (UCRC), University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Sory Ibrahim Diawara
- West African International Center for Excellence in Malaria Research, Techniques and Technologies of Bamako, Bamako, Mali
- University Clinical Research Center (UCRC), University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Daouda Sanogo
- West African International Center for Excellence in Malaria Research, Techniques and Technologies of Bamako, Bamako, Mali
- University Clinical Research Center (UCRC), University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Soumba Keita
- West African International Center for Excellence in Malaria Research, Techniques and Technologies of Bamako, Bamako, Mali
- University Clinical Research Center (UCRC), University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Ibrahim Sanogo
- West African International Center for Excellence in Malaria Research, Techniques and Technologies of Bamako, Bamako, Mali
- University Clinical Research Center (UCRC), University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Cheick Oumar Doumbia
- West African International Center for Excellence in Malaria Research, Techniques and Technologies of Bamako, Bamako, Mali
- University Clinical Research Center (UCRC), University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Bourama Keïta
- West African International Center for Excellence in Malaria Research, Techniques and Technologies of Bamako, Bamako, Mali
- Malaria Research and Training Center (MRTC), University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Amadou Sekou Traoré
- West African International Center for Excellence in Malaria Research, Techniques and Technologies of Bamako, Bamako, Mali
- University Clinical Research Center (UCRC), University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Ibrahim Sissoko
- West African International Center for Excellence in Malaria Research, Techniques and Technologies of Bamako, Bamako, Mali
- Malaria Research and Training Center (MRTC), University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Hamady Coulibaly
- West African International Center for Excellence in Malaria Research, Techniques and Technologies of Bamako, Bamako, Mali
- University Clinical Research Center (UCRC), University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Sidibé M'Baye Thiam
- West African International Center for Excellence in Malaria Research, Techniques and Technologies of Bamako, Bamako, Mali
- University Clinical Research Center (UCRC), University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Alyssa Barry
- Institute for Mental and Physical Health and Clinical Translation (IMPACT) and School of Medicine, Deakin University, Geelong, Australia
| | - Jeffey G. Shaffer
- School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana
| | - Mahamadou Diakite
- West African International Center for Excellence in Malaria Research, Techniques and Technologies of Bamako, Bamako, Mali
- University Clinical Research Center (UCRC), University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Seydou Doumbia
- West African International Center for Excellence in Malaria Research, Techniques and Technologies of Bamako, Bamako, Mali
- University Clinical Research Center (UCRC), University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
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Sa-Ngamuang C, Lawpoolsri S, Su Yin M, Barkowsky T, Cui L, Prachumsri J, Haddawy P. Assessment of malaria risk in Southeast Asia: a systematic review. Malar J 2023; 22:339. [PMID: 37940923 PMCID: PMC10631000 DOI: 10.1186/s12936-023-04772-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 10/26/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND Several countries in Southeast Asia are nearing malaria elimination, yet eradication remains elusive. This is largely due to the challenge of focusing elimination efforts, an area where risk prediction can play an essential supporting role. Despite its importance, there is no standard numerical method to quantify the risk of malaria infection. Thus, there is a need for a consolidated view of existing definitions of risk and factors considered in assessing risk to analyse the merits of risk prediction models. This systematic review examines studies of the risk of malaria in Southeast Asia with regard to their suitability in addressing the challenges of malaria elimination in low transmission areas. METHODS A search of four electronic databases over 2010-2020 retrieved 1297 articles, of which 25 met the inclusion and exclusion criteria. In each study, examined factors included the definition of the risk and indicators of malaria transmission used, the environmental and climatic factors associated with the risk, the statistical models used, the spatial and temporal granularity, and how the relationship between environment, climate, and risk is quantified. RESULTS This review found variation in the definition of risk used, as well as the environmental and climatic factors in the reviewed articles. GLM was widely adopted as the analysis technique relating environmental and climatic factors to malaria risk. Most of the studies were carried out in either a cross-sectional design or case-control studies, and most utilized the odds ratio to report the relationship between exposure to risk and malaria prevalence. CONCLUSIONS Adopting a standardized definition of malaria risk would help in comparing and sharing results, as would a clear description of the definition and method of collection of the environmental and climatic variables used. Further issues that need to be more fully addressed include detection of asymptomatic cases and considerations of human mobility. Many of the findings of this study are applicable to other low-transmission settings and could serve as a guideline for further studies of malaria in other regions.
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Affiliation(s)
- Chaitawat Sa-Ngamuang
- Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand
| | - Saranath Lawpoolsri
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Myat Su Yin
- Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand
| | - Thomas Barkowsky
- Bremen Spatial Cognition Center (BSCC), University of Bremen, Bremen, Germany
| | - Liwang Cui
- Division of Infectious Diseases and International Medicine, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, USA
| | - Jetsumon Prachumsri
- Mahidol Vivax Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Peter Haddawy
- Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand.
- Bremen Spatial Cognition Center (BSCC), University of Bremen, Bremen, Germany.
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Wijerathna T, Gunathilaka N. Time series analysis of leishmaniasis incidence in Sri Lanka: evidence for humidity-associated fluctuations. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2023; 67:275-284. [PMID: 36378349 PMCID: PMC9666979 DOI: 10.1007/s00484-022-02404-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 06/25/2022] [Accepted: 11/04/2022] [Indexed: 06/16/2023]
Abstract
Leishmaniasis is a vector-borne disease of which the transmission is highly influenced by climatic factors, whereas the nature and magnitude differ between geographical regions. The effects of climatic variables on leishmaniasis in Sri Lanka are poorly investigated. The present study focused on time-series analysis of leishmaniasis cases reported from Sri Lanka with selected climatic variables. Variance stabilized time series of leishmaniasis patients of entire Sri Lanka and major districts from 2014 to 2018 was fitted to autoregressive integrated moving average (ARIMA) models. All the possible models were generated by assigning different values for autoregression and moving average terms using a function written in R statistical program. The top ten models with the lowest Akaike information criterion (AIC) values were selected by writing another function. These models were further evaluated using RMSE and MAPE error parameters to select the optimal model for each area. Cross-autocorrelation analyses were performed to assess the associations between climate and the leishmaniasis incidence. Most associated lags of each variable were integrated into the optimal models to determine the true effects imposed. The optimal models varied depending on the area. SARIMA (0,1,1) (1,0,0)12 was optimal for the country level. All the forecasts were within the 95% confidence intervals. Humidity was the most notable factor associated with leishmaniasis, which could be attributed to the positive impacts on sand fly activity. Rainfall showed a negative impact probably as a result of flooding of sand fly larval habitats. The ARIMA-based models performed well for the prediction of leishmaniasis in the short term.
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Affiliation(s)
- Tharaka Wijerathna
- Department of Parasitology, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
| | - Nayana Gunathilaka
- Department of Parasitology, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
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The malaria transmission in Anhui province China. Infect Dis Model 2022; 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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Baharom M, Soffian SSS, Peng CS, Baharudin MH, Mirza U, Madrim MF, Jeffree MS, Rahim SSSA, Hassan MR. Projecting Malaria Incidence Based on Climate Change Modeling Approach: A Systematic Review. Open Access Maced J Med Sci 2022. [DOI: 10.3889/oamjms.2022.10141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND: Climate change will affect the transmission of malaria by shifting the geographical space of the vector.
AIM: The review aims to examine the climate change modeling approach and climatic variables used for malaria projection.
METHODS: Articles were systematically searched from four databases, Scopus, Web of Science, PubMed, and SAGE. The PICO concept was used for formulation search and PRISMA approach to identify the final articles.
RESULTS: A total of 27 articles were retrieved and reviewed. There were six climate factors identified in this review: Temperature, rainfall/precipitation, humidity, wind, solar radiation, and climate change scenarios. Modeling approaches used to project future malarial trend includes mathematical and computational approach.
CONCLUSION: This review provides robust evidence of an association between the impact of climate change and malaria incidence. Prediction on seasonal patterns would be useful for malaria surveillance in public health prevention and mitigation strategies.
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Nyasa RB, Awatboh F, Kwenti TE, Titanji VPK, Ayamba NLM. The effect of climatic factors on the number of malaria cases in an inland and a coastal setting from 2011 to 2017 in the equatorial rain forest of Cameroon. BMC Infect Dis 2022; 22:461. [PMID: 35562702 PMCID: PMC9101852 DOI: 10.1186/s12879-022-07445-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 05/05/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Weather fluctuation affects the incidence of malaria through a network of causuative pathays. Globally, human activities have ultered weather conditions over time, and consequently the number of malaria cases. This study aimed at determining the influence of humidity, temperature and rainfall on malaria incidence in an inland (Muyuka) and a coastal (Tiko) settings for a period of seven years (2011-2017) as well as predict the number of malaria cases two years after (2018 and 2019). METHODS Malaria data for Muyuka Health District (MHD) and Tiko Health District (THD) were obtained from the Regional Delegation of Public Health and Tiko District Health service respectively. Climate data for MHD was obtained from the Regional Delegation of Transport while that of THD was gotten from Cameroon Development Coorporation. Spearman rank correlation was used to investigate the relationship between number of malaria cases and the weather variables and the simple seasonal model was used to forecast the number of malaria cases for 2018 and 2019. RESULTS The mean monthly rainfall, temperature and relative humidity for MHD were 200.38 mm, 27.050C, 82.35% and THD were 207.36 mm, 27.57 °C and 84.32% respectively, with a total number of malaria cases of 56,745 and 40,160. In MHD, mean yearly humidity strongly correlated negatively with number of malaria cases (r = - 0.811, p = 0.027) but in THD, a moderate negative yearly correlation was observed (r = - 0.595, p = 0.159). In THD, the mean seasonal temperature moderately correlated (r = 0.599, p = 0.024) positively with the number of malaria cases, whereas MHD had a very weak negative correlation (r = - 0.174, p = 0.551). Likewise mean seasonal rainfall in THD moderately correlated (r = - 0.559, p = 0.038) negatively with malaria cases, contrary to MHD which showed a very weak positive correlation (r = 0.425, p = 0.130). The simple seasonal model predicted 6,842 malaria cases in Muyuka, for 2018 and same number for 2019, while 3167 cases were observed in 2018 and 2848 in 2019. Also 6,738 cases of malaria were predicted for MHD in 2018 likewise 2019, but 7327 cases were observed in 2018 and 21,735 cases in 2019. CONCLUSION Humidity is the principal climatic variable that negatively influences malaria cases in MHD, while higher seasonal temperatures and lower seasonal rain fall significantly increase malaria cases in THD.
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Affiliation(s)
- Raymond Babila Nyasa
- Department of Microbiology and Parasitology, University of Buea, P. O. Box 63, Buea, Cameroon.
- Biotechnology Unit, Faculty of Science, University of Buea, P.O. Box 63, Buea, South West Region, Cameroon.
| | - Fuanyi Awatboh
- Department of Microbiology and Parasitology, University of Buea, P. O. Box 63, Buea, Cameroon
| | - Tebit Emmanuel Kwenti
- Department of Medical Laboratory Sciences, Faculty of Health Science, University of Buea, P.O. Box 63, Buea, South West Region, Cameroon
| | - Vincent P K Titanji
- Biotechnology Unit, Faculty of Science, University of Buea, P.O. Box 63, Buea, South West Region, Cameroon
- Cameroon Christian University, P.O. Box 5, Bali, North West Region, Cameroon
| | - Ndip Lucy M Ayamba
- Department of Microbiology and Parasitology, University of Buea, P. O. Box 63, Buea, Cameroon
- Laboratory for Emerging Infectious Diseases, University of Buea, P.O.Box 63, Buea, South West Region, Cameroon
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Tegegne E, Alemu Gelaye K, Dessie A, Shimelash A, Asmare B, Deml YA, Lamore Y, Temesgen T, Demissie B, Teym A. Spatio-Temporal Variation of Malaria Incidence and Risk Factors in West Gojjam Zone, Northwest Ethiopia. ENVIRONMENTAL HEALTH INSIGHTS 2022; 16:11786302221095702. [PMID: 35558819 PMCID: PMC9087229 DOI: 10.1177/11786302221095702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/25/2022] [Indexed: 06/15/2023]
Abstract
Introduction Malaria is a life-threatening acute febrile illness which is affecting the lives of millions globally. Its distribution is characterized by spatial, temporal, and spatiotemporal heterogeneity. Detection of the space-time distribution and mapping high-risk areas is useful to target hot spots for effective intervention. Methods Time series cross sectional study was conducted using weekly malaria surveillance data obtained from Amhara Public Health Institute. Poisson model was fitted to determine the purely spatial, temporal, and space-time clusters using SaTScan™ 9.6 software. Spearman correlation, bivariate, and multivariable negative binomial regressions were used to analyze the relation of the climatic factors to count of malaria incidence. Result Jabitenan, Quarit, Sekela, Bure, and Wonberma were high rate spatial cluster of malaria incidence hierarchically. Spatiotemporal clusters were detected. A temporal scan statistic identified 1 risk period from 1 July 2013 to 30 June 2015. The adjusted incidence rate ratio showed that monthly average temperature and monthly average rainfall were independent predictors for malaria incidence at all lag-months. Monthly average relative humidity was significant at 2 months lag. Conclusion Malaria incidence had spatial, temporal, spatiotemporal variability in West Gojjam zone. Mean monthly temperature and rainfall were directly and negatively associated to count of malaria incidence respectively. Considering these space-time variations and risk factors (temperature and rainfall) would be useful for the prevention and control and ultimately achieve elimination.
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Affiliation(s)
- Eniyew Tegegne
- Department of Environmental Health,
College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia
| | - Kassahun Alemu Gelaye
- Institutes of Public Health, College of
Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Awrajaw Dessie
- Institutes of Public Health, College of
Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Alebachew Shimelash
- Department of Environmental Health,
College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia
| | - Biachew Asmare
- Department of Human Nutrition, College
of Health Sciences, Debre Markos University, Debre Markos, Ethiopia
| | - Yikeber Argachew Deml
- Department of Biomedical Sciences,
School of Medicine, Debre Markos University, Ethiopia
| | - Yonas Lamore
- Department of Environmental Health,
College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia
| | - Tegegne Temesgen
- Department of Environmental Health,
College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia
| | - Biruk Demissie
- Department of Environmental Health,
College of Health Science, Debre Tabor University, Debre Tabor, Ethiopia
| | - Abraham Teym
- Department of Environmental Health,
College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia
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Chaudhary S, Soman B. Spatiotemporal analysis of environmental and physiographic factors related to malaria in Bareilly district, India. Osong Public Health Res Perspect 2022; 13:123-132. [PMID: 35538684 PMCID: PMC9091635 DOI: 10.24171/j.phrp.2021.0304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 02/16/2022] [Indexed: 12/04/2022] Open
Abstract
Objectives The aim of this study was to explore the spatiotemporal clustering of reported malaria cases and to study the effects of various environmental and physiographic factors on malaria incidence in Bareilly district, Uttar Pradesh, India. Methods Malaria surveillance data were collected from the state health department and cleaned into an analyzable format. These data were analyzed along with meteorological, physiographic, and 2019 population data, which were obtained from the Indian Meteorological Department, National Aeronautics and Space Administration web portal, the Bhuvan platform of the Indian Space Research Organization, and the 2011 Census of India. Results In total, 46,717 malaria cases were reported in Bareilly district in 2019, of which 25.99% were Plasmodium vivax cases and 74.01% were P. falciparum cases. The reported malaria cases in the district showed clustering, with significant spatial autocorrelation (Moran’s I value=0.63), and space-time clustering (p<0.01). A significant positive correlation was found between monthly malaria incidence and the monthly mean temperature (with a lag of 1−2 months) and rainfall (with a lag of 1 month). A significant negative correlation was detected between the elevation of blocks (i.e., intermediate-level administrative districts) and annual malaria reporting. Conclusion The presence of space-time clustering of malaria cases and its correlation with meteorological and physiographic factors indicate that routine spatial analysis of the surveillance data could help control and manage malaria outbreaks in the district.
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Kamana E, Zhao J, Bai D. Predicting the impact of climate change on the re-emergence of malaria cases in China using LSTMSeq2Seq deep learning model: a modelling and prediction analysis study. BMJ Open 2022; 12:e053922. [PMID: 35361642 PMCID: PMC8971767 DOI: 10.1136/bmjopen-2021-053922] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVES Malaria is a vector-borne disease that remains a serious public health problem due to its climatic sensitivity. Accurate prediction of malaria re-emergence is very important in taking corresponding effective measures. This study aims to investigate the impact of climatic factors on the re-emergence of malaria in mainland China. DESIGN A modelling study. SETTING AND PARTICIPANTS Monthly malaria cases for four Plasmodium species (P. falciparum, P. malariae, P. vivax and other Plasmodium) and monthly climate data were collected for 31 provinces; malaria cases from 2004 to 2016 were obtained from the Chinese centre for disease control and prevention and climate parameters from China meteorological data service centre. We conducted analyses at the aggregate level, and there was no involvement of confidential information. PRIMARY AND SECONDARY OUTCOME MEASURES The long short-term memory sequence-to-sequence (LSTMSeq2Seq) deep neural network model was used to predict the re-emergence of malaria cases from 2004 to 2016, based on the influence of climatic factors. We trained and tested the extreme gradient boosting (XGBoost), gated recurrent unit, LSTM, LSTMSeq2Seq models using monthly malaria cases and corresponding meteorological data in 31 provinces of China. Then we compared the predictive performance of models using root mean squared error (RMSE) and mean absolute error evaluation measures. RESULTS The proposed LSTMSeq2Seq model reduced the mean RMSE of the predictions by 19.05% to 33.93%, 18.4% to 33.59%, 17.6% to 26.67% and 13.28% to 21.34%, for P. falciparum, P. vivax, P. malariae, and other plasmodia, respectively, as compared with other candidate models. The LSTMSeq2Seq model achieved an average prediction accuracy of 87.3%. CONCLUSIONS The LSTMSeq2Seq model significantly improved the prediction of malaria re-emergence based on the influence of climatic factors. Therefore, the LSTMSeq2Seq model can be effectively applied in the malaria re-emergence prediction.
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Affiliation(s)
- Eric Kamana
- Complexity Science Institute, School of Automation, Qingdao University, Qingdao, China
| | - Jijun Zhao
- Complexity Science Institute, School of Automation, Qingdao University, Qingdao, China
| | - Di Bai
- Complexity Science Institute, School of Automation, Qingdao University, Qingdao, China
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11
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Ost K, Berrang-Ford L, Bishop-Williams K, Charette M, Harper SL, Lwasa S, Namanya DB, Huang Y, Katz AB, Ebi K. Do socio-demographic factors modify the effect of weather on malaria in Kanungu District, Uganda? Malar J 2022; 21:98. [PMID: 35317835 PMCID: PMC8939205 DOI: 10.1186/s12936-022-04118-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 03/05/2022] [Indexed: 11/25/2022] Open
Abstract
Background There is concern in the international community regarding the influence of climate change on weather variables and seasonality that, in part, determine the rates of malaria. This study examined the role of sociodemographic variables in modifying the association between temperature and malaria in Kanungu District (Southwest Uganda). Methods Hospital admissions data from Bwindi Community Hospital were combined with meteorological satellite data from 2011 to 2014. Descriptive statistics were used to describe the distribution of malaria admissions by age, sex, and ethnicity (i.e. Bakiga and Indigenous Batwa). To examine how sociodemographic variables modified the association between temperature and malaria admissions, this study used negative binomial regression stratified by age, sex, and ethnicity, and negative binomial regression models that examined interactions between temperature and age, sex, and ethnicity. Results Malaria admission incidence was 1.99 times greater among Batwa than Bakiga in hot temperature quartiles compared to cooler temperature quartiles, and that 6–12 year old children had a higher magnitude of association of malaria admissions with temperature compared to the reference category of 0–5 years old (IRR = 2.07 (1.40, 3.07)). Discussion Results indicate that socio-demographic variables may modify the association between temperature and malaria. In some cases, such as age, the weather-malaria association in sub-populations with the highest incidence of malaria in standard models differed from those most sensitive to temperature as found in these stratified models. Conclusion The effect modification approach used herein can be used to improve understanding of how changes in weather resulting from climate change might shift social gradients in health. Supplementary Information The online version contains supplementary material available at 10.1186/s12936-022-04118-5.
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Affiliation(s)
- Katarina Ost
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada.
| | - Lea Berrang-Ford
- Priestley International Centre for Climate, University of Leeds, Leeds, UK
| | | | - Margot Charette
- Department of Geography, McGill University, Montreal, Canada
| | | | - Shuaib Lwasa
- Department of Geography, Geo-Informatics and Climatic Sciences, School of Forestry, Environmental and Geographical Sciences, College of Agricultural and Environmental Sciences, Makerere University, Kampala, Uganda
| | - Didacus B Namanya
- Indigenous Health Adaptation To Climate Change, Research Team, Edmonton, Canada.,Uganda Martyrs University, Kampala, Uganda.,Faculty of Health Sciences, Uganda Martyrs University, Kampala, Uganda
| | - Yi Huang
- Department of Atmospheric and Ocean Sciences, McGill University, Montreal, Canada
| | - Aaron B Katz
- Department of Health Services, University of Washington, Seattle, USA
| | | | | | - Kristie Ebi
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada.,Priestley International Centre for Climate, University of Leeds, Leeds, UK.,School of Interdisciplinary Science, McMaster University, Hamilton, Canada.,Department of Geography, McGill University, Montreal, Canada.,School of Public Health, University of Alberta, Edmonton, Canada.,Department of Geography, Geo-Informatics and Climatic Sciences, School of Forestry, Environmental and Geographical Sciences, College of Agricultural and Environmental Sciences, Makerere University, Kampala, Uganda.,Indigenous Health Adaptation To Climate Change, Research Team, Edmonton, Canada.,Uganda Martyrs University, Kampala, Uganda.,Faculty of Health Sciences, Uganda Martyrs University, Kampala, Uganda.,Department of Atmospheric and Ocean Sciences, McGill University, Montreal, Canada.,Department of Health Services, University of Washington, Seattle, USA.,Bwindi Community Hospital, Kanungu, Uganda.,Center for Health and the Global Environment, University of Washington, Seattle, USA
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12
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Analysis of bluetongue disease epizootics in sheep of Andhra Pradesh, India using spatial and temporal autocorrelation. Vet Res Commun 2022; 46:967-978. [PMID: 35194693 DOI: 10.1007/s11259-022-09902-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 02/10/2022] [Indexed: 10/19/2022]
Abstract
Bluetongue (BT) disease poses a constant risk to the livestock population around the world. A better understanding of the risk factors will enable a more accurate prediction of the place and time of high-risk events. Mapping the disease epizootics over a period in a particular geographic area will identify the spatial distribution of disease occurrence. A Geographical Information System (GIS) based methodology to analyze the relationship between bluetongue epizootics and spatial-temporal patterns was used for the years 2000 to 2015 in sheep of Andhra Pradesh, India. Autocorrelation (ACF), partial autocorrelation (PACF), and cross-correlation (CCF) analyses were carried out to find the self-dependency between BT epizootics and their dependencies on environmental factors and livestock population. The association with climatic or remote sensing variables at different months lag, including wind speed, temperature, rainfall, relative humidity, normalized difference vegetation index (NDVI), normalized difference water index (NDWI), land surface temperature (LST), was also examined. The ACF & PACF of BT epizootics with its lag showed a significant positive autocorrelation with a month's lag (r = 0.41). Cross-correlations between the environmental variables and BT epizootics indicated the significant positive correlations at 0, 1, and 2 month's lag of rainfall, relative humidity, normalized difference water index (NDWI), and normalized difference vegetation index (NDVI). Spatial autocorrelation analysis estimated the univariate global Moran's I value of 0.21. Meanwhile, the local Moran's I value for the year 2000 (r = 0.32) showed a high degree of spatial autocorrelation. The spatial autocorrelation analysis revealed that the BT epizootics in sheep are having considerable spatial association among the outbreaks in nearby districts, and have to be taken care of while making any forecasting or disease prediction with other risk factors.
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Guo J, Wang A, Zhou W, Gong Y, Smith SR. Discrete epidemic modelling of COVID-19 transmission in Shaanxi Province with media reporting and imported cases. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:1388-1410. [PMID: 35135209 DOI: 10.3934/mbe.2022064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The large-scale infection of COVID-19 has led to a significant impact on lives and economies around the world and has had considerable impact on global public health. Social distancing, mask wearing and contact tracing have contributed to containing or at least mitigating the outbreak, but how public awareness influences the effectiveness and efficiency of such approaches remains unclear. In this study, we developed a discrete compartment dynamic model to mimic and explore how media reporting and the strengthening containment strategies can help curb the spread of COVID-19 using Shaanxi Province, China, as a case study. The targeted model is parameterized based on multi-source data, including the cumulative number of confirmed cases, recovered individuals, the daily number of media-reporting items and the imported cases from the rest of China outside Shaanxi from January 23 to April 11, 2020. We carried out a sensitivity analysis to investigate the effect of media reporting and imported cases on transmission. The results revealed that reducing the intensity of media reporting, which would result in a significant increasing of the contact rate and a sizable decreasing of the contact-tracing rate, could aggravate the outbreak severity by increasing the cumulative number of confirmed cases. It also demonstrated that diminishing the imported cases could alleviate the outbreak severity by reducing the length of the epidemic and the final size of the confirmed cases; conversely, delaying implementation of lockdown strategies could prolong the length of the epidemic and magnify the final size. These findings suggest that strengthening media coverage and timely implementing of lockdown measures can significantly reduce infection.
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Affiliation(s)
- Jin Guo
- School of Mathematics and Information Science, Baoji University of Arts and Sciences, Baoji 721013, China
| | - Aili Wang
- School of Mathematics and Information Science, Baoji University of Arts and Sciences, Baoji 721013, China
| | - Weike Zhou
- College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, China
| | - Yinjiao Gong
- School of Mathematics and Information Science, Baoji University of Arts and Sciences, Baoji 721013, China
| | - Stacey R Smith
- Department of Mathematics and Faculty of Medicine, The University of Ottawa, Ottawa ON K1N 6N5, Canada
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14
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Singh N, Mall RK, Banerjee T, Gupta A. Association between climate and infectious diseases among children in Varanasi city, India: A prospective cohort study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 796:148769. [PMID: 34274660 DOI: 10.1016/j.scitotenv.2021.148769] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 06/25/2021] [Accepted: 06/27/2021] [Indexed: 06/13/2023]
Abstract
The effects of climate on infectious diseases could influence the health impacts, particularly in children in countries with the unfair socioeconomic conditions. In a prospective cohort of 461 children under 16-years-of-age in Varanasi city, India, the association of maximum-temperature (Tmax), relative humidity (RH), absolute humidity (AH), rainfall (RF), wind-speed (WS), and solar radiation (SLR) with prevalent infectious diseases (Diarrhea, Common cold and flu, Pneumonia, Skin-disease and Malaria, and Dengue) was examined using binomial-regression, adjusting for confounders and effect modifiers (socioeconomic-status; SES and child anthropometry), from January 2017 to January 2020. Attributable-fraction (AFx) was calculated due to each climate variable for each infectious disease. The result showed that each unit (1 °C) rise in Tmax was associated with an increase in diarrhea and skin-disease cases by 3.97% (95% CI: 2.92, 5.02) and 3.94% (95% CI: 1.67, 6.22), respectively, whereas, a unit decline in Tmax was associated with an increase in cold and flu cases by 3.87% (95% CI: 2.97, 4.76). Rise in humidity (RH) was associated with increase in cases of cold and flu by 0.73% (95% CI: 0.38, 1.08) and malaria (AH) by 7.19% (95% CI: 1.51, 12.87) while each unit (1 g/m3) decrease in humidity (AH) observed increase in pneumonia cases by 3.02% (95% CI: 0.75, 5.3). WS was positively associated with diarrhea (14.16%; 95% CI: 6.52, 21.80) and negatively with dengue (17.40%; 12.32, 22.48) cases for each unit change (kmph). RF showed marginal association while SLR showed no association at all. The combined AFx due to climatic factors ranged from 9 to 18%. SES and anthropometric parameters modified the climate-morbidity association in children with a high proportion of children found suffering from stunting, wasting, and underweight conditions. Findings from this study draw the attention of government and policymakers to prioritize effective measures for child health as the present association may increase disease burden in the future under climate-change scenarios in already malnourished paediatric population through multiple pathways.
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Affiliation(s)
- Nidhi Singh
- DST-Mahamana Centre of Excellence in Climate Change Research, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India
| | - R K Mall
- DST-Mahamana Centre of Excellence in Climate Change Research, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India.
| | - T Banerjee
- DST-Mahamana Centre of Excellence in Climate Change Research, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India
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Malaria in Cambodia: A Retrospective Analysis of a Changing Epidemiology 2006-2019. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18041960. [PMID: 33670471 PMCID: PMC7922556 DOI: 10.3390/ijerph18041960] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 01/22/2021] [Accepted: 02/12/2021] [Indexed: 11/17/2022]
Abstract
Background: In Cambodia, malaria persists with changing epidemiology and resistance to antimalarials. This study aimed to describe how malaria has evolved spatially from 2006 to 2019 in Cambodia. Methods: We undertook a secondary analysis of existing malaria data from all government healthcare facilities in Cambodia. The epidemiology of malaria was described by sex, age, seasonality, and species. Spatial clusters at the district level were identified with a Poisson model. Results: Overall, incidence decreased from 7.4 cases/1000 population in 2006 to 1.9 in 2019. The decrease has been drastic for females, from 6.7 to 0.6/1000. Adults aged 15–49 years had the highest malaria incidence among all age groups. The proportion of Plasmodium (P.) falciparum + Mixed among confirmed cases declined from 87.9% (n = 67,489) in 2006 to 16.6% (n = 5290) in 2019. Clusters of P. falciparum + Mixed and P. vivax + Mixed were detected in forested provinces along all national borders. Conclusions: There has been a noted decrease in P. falciparum cases in 2019, suggesting that an intensification plan should be maintained. A decline in P. vivax cases was also noted, although less pronounced. Interventions aimed at preventing new infections of P. vivax and relapses should be prioritized. All detected malaria cases should be captured by the national surveillance system to avoid misleading trends.
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16
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Lau K, Dorigatti I, Miraldo M, Hauck K. SARIMA-modelled greater severity and mortality during the 2010/11 post-pandemic influenza season compared to the 2009 H1N1 pandemic in English hospitals. Int J Infect Dis 2021; 105:161-171. [PMID: 33548552 DOI: 10.1016/j.ijid.2021.01.070] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 01/26/2021] [Accepted: 01/29/2021] [Indexed: 10/22/2022] Open
Abstract
OBJECTIVE The COVID-19 pandemic demonstrates the need for understanding pathways to healthcare demand, morbidity, and mortality of pandemic patients. We estimate H1N1 (1) hospitalization rates, (2) severity rates (length of stay, ventilation, pneumonia, and death) of those hospitalized, (3) mortality rates, and (4) time lags between infections and hospitalizations during the pandemic (June 2009 to March 2010) and post-pandemic influenza season (November 2010 to February 2011) in England. METHODS Estimates of H1N1 infections from a dynamic transmission model are combined with hospitalizations and severity using time series econometric analyses of administrative patient-level hospital data. RESULTS Hospitalization rates were 34% higher and severity rates of those hospitalized were 20%-90% higher in the post-pandemic period than the pandemic. Adults (45-64-years-old) had the highest ventilation and pneumonia hospitalization rates. Hospitalizations did not lag infection during the pandemic for the young (<24-years-old) but lagged by one or more weeks for all ages in the post-pandemic period. DISCUSSION The post-pandemic flu season exhibited heightened H1N1 severity, long after the pandemic was declared over. Policymakers should remain vigilant even after pandemics seem to have subsided. Analysis of administrative hospital data and epidemiological modelling estimates can provide valuable insights to inform responses to COVID-19 and future influenza and other disease pandemics.
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Affiliation(s)
- Krystal Lau
- Imperial College Business School: Department of Economics & Public Policy; Centre for Health Economics & Policy Innovation, London, United Kingdom SW7 2AZ.
| | - Ilaria Dorigatti
- Imperial College London: MRC Centre for Global Infectious Disease Analysis (MRC GIDA), Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, London, United Kingdom W2 1PG
| | - Marisa Miraldo
- Imperial College Business School: Department of Economics & Public Policy; Centre for Health Economics & Policy Innovation, London, United Kingdom SW7 2AZ
| | - Katharina Hauck
- Imperial College London: MRC Centre for Global Infectious Disease Analysis (MRC GIDA), Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, London, United Kingdom W2 1PG
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17
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Keshavarz H, Hassanpour G, Tohidinik H, Mohebali M, Sanjar M. Prediction of malaria cases in the southeastern Iran using climatic variables: An 18-year SARIMA time series analysis. ASIAN PAC J TROP MED 2021. [DOI: 10.4103/1995-7645.329008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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18
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Emeto TI, Adegboye OA, Rumi RA, Khan MUI, Adegboye M, Khan WA, Rahman M, Streatfield PK, Rahman KM. Disparities in Risks of Malaria Associated with Climatic Variability among Women, Children and Elderly in the Chittagong Hill Tracts of Bangladesh. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E9469. [PMID: 33348771 PMCID: PMC7766360 DOI: 10.3390/ijerph17249469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 12/09/2020] [Accepted: 12/15/2020] [Indexed: 12/04/2022]
Abstract
Malaria occurrence in the Chittagong Hill Tracts in Bangladesh varies by season and year, but this pattern is not well characterized. The role of environmental conditions on the occurrence of this vector-borne parasitic disease in the region is not fully understood. We extracted information on malaria patients recorded in the Upazila (sub-district) Health Complex patient registers of Rajasthali in Rangamati district of Bangladesh from February 2000 to November 2009. Weather data for the study area and period were obtained from the Bangladesh Meteorological Department. Non-linear and delayed effects of meteorological drivers, including temperature, relative humidity, and rainfall on the incidence of malaria, were investigated. We observed significant positive association between temperature and rainfall and malaria occurrence, revealing two peaks at 19 °C (logarithms of relative risks (logRR) = 4.3, 95% CI: 1.1-7.5) and 24.5 °C (logRR = 4.7, 95% CI: 1.8-7.6) for temperature and at 86 mm (logRR = 19.5, 95% CI: 11.7-27.3) and 284 mm (logRR = 17.6, 95% CI: 9.9-25.2) for rainfall. In sub-group analysis, women were at a much higher risk of developing malaria at increased temperatures. People over 50 years and children under 15 years were more susceptible to malaria at increased rainfall. The observed associations have policy implications. Further research is needed to expand these findings and direct resources to the vulnerable populations for malaria prevention and control in the Chittagong Hill Tracts of Bangladesh and the region with similar settings.
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Affiliation(s)
- Theophilus I. Emeto
- Public Health & Tropical Medicine, College of Public Health, Medical & Veterinary Sciences, James Cook University, Townsville, QLD 4811, Australia;
| | - Oyelola A. Adegboye
- Public Health & Tropical Medicine, College of Public Health, Medical & Veterinary Sciences, James Cook University, Townsville, QLD 4811, Australia;
- Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, QLD 4811, Australia
| | - Reza A. Rumi
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, Bangladesh; (R.A.R.); (M.-U.I.K.); (W.A.K.); (P.K.S.)
| | - Mahboob-Ul I. Khan
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, Bangladesh; (R.A.R.); (M.-U.I.K.); (W.A.K.); (P.K.S.)
| | | | - Wasif A. Khan
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, Bangladesh; (R.A.R.); (M.-U.I.K.); (W.A.K.); (P.K.S.)
| | - Mahmudur Rahman
- Institute of Epidemiology, Disease Control and Research (IEDCR), Dhaka 1212, Bangladesh;
| | - Peter K. Streatfield
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, Bangladesh; (R.A.R.); (M.-U.I.K.); (W.A.K.); (P.K.S.)
| | - Kazi M. Rahman
- North Coast Public Health Unit, New South Wales Health, Lismore, NSW 2480, Australia;
- The University of Sydney, University Centre for Rural Health, Lismore, NSW 2480, Australia
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Ma Y, Zhang Y, Cheng B, Feng F, Jiao H, Zhao X, Ma B, Yu Z. A review of the impact of outdoor and indoor environmental factors on human health in China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2020; 27:42335-42345. [PMID: 32833174 DOI: 10.1007/s11356-020-10452-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 08/09/2020] [Indexed: 06/11/2023]
Abstract
The Intergovernmental Panel on Climate Change (IPCC) reported that global climate change has led to the increased occurrence of extreme weather events. In the context of global climate change, more evidence indicates that abnormal meteorological conditions could increase the risk of epidemiological mortality and morbidity. In this study, using a systematic review, we evaluated a total of 175 studies (including 158 studies on outdoor environment and 17 studies on indoor environment) to summarize the impact of outdoor and indoor environment on human health in China using the database of PubMed, Web of Science, the Cochrane Library, and Embase. In particular, we focused on studies about cardiovascular and respiratory mortality and morbidity, the prevalence of digestive system diseases, infectious diseases, and preterm birth. Most of the studies we reviewed were conducted in three of the metropolises of China, including Beijing, Guangzhou, and Shanghai. For the outdoor environment, we summarized the effects of climate change-related phenomena on health, including ambient air temperature, diurnal temperature range (DTR), temperature extremes, and so on. Studies on the associations between temperature and human health accounted for 79.7% of the total studies reviewed. We also screened out 19 articles to explore the effect of air temperature on cardiovascular diseases in different cities in the final meta-analysis. Besides, modern lifestyle involves a large amount of time spent indoors; therefore, indoor environment also plays an important role in human health. Nevertheless, studies on the impact of indoor environment on human health are rarely reported in China. According to the limited reports, adverse indoor environment could impose a high health risk on children.
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Affiliation(s)
- Yuxia Ma
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China.
| | - Yifan Zhang
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Bowen Cheng
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Fengliu Feng
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Haoran Jiao
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Xiaoyan Zhao
- Neurology Department, General Hospital of the Chinese People's Liberation Army, Beijing, 100000, China
| | - Bingji Ma
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Zhiang Yu
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
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Yan Q, Tang Y, Yan D, Wang J, Yang L, Yang X, Tang S. Impact of media reports on the early spread of COVID-19 epidemic. J Theor Biol 2020; 502:110385. [PMID: 32593679 PMCID: PMC7316072 DOI: 10.1016/j.jtbi.2020.110385] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 06/15/2020] [Accepted: 06/17/2020] [Indexed: 11/16/2022]
Abstract
Media reports can modify people's knowledge of emerging infectious diseases, and thus changing the public attitudes and behaviors. However, how the media reports affect the development of COVID-19 epidemic is a key public health issue. Here the Pearson correlation and cross-correlation analyses are conducted to find the statistically significant correlations between the number of new hospital notifications for COVID-19 and the number of daily news items for twelve major websites in China from January 11th to February 6th 2020. To examine the implication for transmission dynamics of these correlations, we proposed a novel model, which embeds the function of individual behaviour change (media impact) into the intensity of infection. The nonlinear least squares estimation is used to identify the best-fit parameter values in the model from the observed data. To determine impact of key parameters with media impact and control measures for the later outcome of the outbreak, we also carried out the uncertainty and sensitivity analyses. These findings confirm the importance of the responses of individuals to the media reports, and the crucial role of experts and governments in promoting the public under self-quarantine. Therefore, for mitigating epidemic COVID-19, the media publicity should be focused on how to guide people's behavioral changes by experts, and the management departments and designated hospitals of the COVID-19 should take effective quarantined measures, which are critical for the control of the disease.
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Affiliation(s)
- Qinling Yan
- School of Science, Chang'an University, Xi'an 710064, PR China
| | - Yingling Tang
- College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, PR China
| | - Dingding Yan
- College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, PR China
| | - Jiaying Wang
- College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, PR China
| | - Linqian Yang
- College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, PR China
| | - Xinpei Yang
- College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, PR China
| | - Sanyi Tang
- College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, PR China.
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Meteorological Factors and Swine Erysipelas Transmission in Southern China. ACTA VET-BEOGRAD 2020. [DOI: 10.2478/acve-2020-0002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Abstract
Swine erysipelas (SE) is one of the best-known and most serious diseases that affect domestic pigs, which is caused by Erysipelothrix rhusiopathiae. It is endemic in Nanning and has been circulating for decades, causing considerable economic losses. The aim of this study was to investigate the effect of meteorological-related variations on the epidemiology of swine erysipelas in Nanning City, a subtropical city of China. Data on monthly counts of reported swine erysipelas and climate data in Nanning are provided by the authorities over the period from 2006 to 2015. Cross-correlation analysis was applied to identify the lag effects of meteorological variables. A zero-inflated negative binomial (ZINB) regression model was used to evaluate the independent contribution of meteorological factors to SE transmission. After controlling seasonality, autocorrelation and lag effects, the results of the model indicated that Southern Oscillation Index (SOI) has a positive effect on SE transmission. Moreover, there is a positive correlation between monthly mean maximum temperature and relative humidity at 0-1 month lag and the number of cases. Furthermore, there is a positive association between the number of SE incidences and precipitation, with a lagged effect of 2 months. In contrast, monthly mean wind velocity negatively correlated with SE of the current month. These findings indicate that meteorological variables may play a significant role in SE transmission in southern China. Finally, more public health actions should be taken to prevent and control the increase of SE disease with consideration of local weather variations.
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Modeling an association between malaria cases and climate variables for Keonjhar district of Odisha, India: a Bayesian approach. J Parasit Dis 2020; 44:319-331. [PMID: 32508406 DOI: 10.1007/s12639-020-01210-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 03/03/2020] [Indexed: 01/05/2023] Open
Abstract
Malaria, a vector-borne disease, is a significant public health problem in Keonjhar district of Odisha (the malaria capital of India). Prediction of malaria, in advance, is an urgent need for reporting rolling cases of disease throughout the year. The climate condition do play an essential role in the transmission of malaria. Hence, the current study aims to develop and assess a simple and straightforward statistical model of an association between malaria cases and climate variates. It may help in accurate predictions of malaria cases given future climate conditions. For this purpose, a Bayesian Gaussian time series regression model is adopted to fit a relationship of the square root of malaria cases with climate variables with practical lag effects. The model fitting is assessed using a Bayesian version of R2 (RsqB). Whereas, the predictive ability of the model is measured using a cross-validation technique. As a result, it is found that the square root of malaria cases with lag 1, maximum temperature, and relative humidity with lag 3 and 0 (respectively), are significantly positively associated with the square root of the cases. However, the minimum and average temperatures with lag 2, respectively, are observed as negatively (significantly) related. The considered model accounts for moderate amount of variation in the square root of malaria cases as received through the results for RsqB. We also present Absolute Percentage Errors (APE) for each of the 12 months (January-December) for a better understanding of the seasonal pattern of the predicted (square root of) malaria cases. Most of the APEs obtained corresponding to test data points is reasonably low. Further, the analysis shows that the considered model closely predicted the actual (square root of) malaria cases, except for some peak cases during the particular months. The output of the current research might help the district to develop and strengthen early warning prediction of malaria cases for proper mitigation, eradication, and prevention in similar settings.
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Demari-Silva B, Laporta GZ, Oliveira T, Sallum M. Plasmodium infection in Kerteszia cruzii (Diptera: Culicidae) in the Atlantic tropical rain forest, southeastern Brazil. INFECTION GENETICS AND EVOLUTION 2019; 78:104061. [PMID: 31683005 DOI: 10.1016/j.meegid.2019.104061] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 09/28/2019] [Accepted: 10/02/2019] [Indexed: 01/19/2023]
Abstract
In Southeastern Brazil, Kerteszia cruzii (former Anopheles cruzii), a bromeliad mosquito species, is considered an efficient human Plasmodium spp. vector. In this region, recent studies showed asymptomatic or sub-patent Plasmodium falciparum infection. In areas of the Atlantic coast in Rio de Janeiro, Plasmodium simium infection was recently reported in both human and howler monkey. Considering that (1) few malaria cases are reported each year in areas across the tropical Atlantic rain forest in southeastern Brazil; (2) malaria elimination in Atlantic forest is challenged by circulation of P. falciparum and P. simium in humans; (3) the complexity of malaria epidemiology in this region; and (4) the public health importance of Kerteszia cruzii as a sylvatic vector; the major goal of this study is to evaluate Plasmodium infection in Ke. cruzii. Mosquito sampling collections were conducted in Esteiro do Morro and Sítio Itapuan, in Cananeia municipality, and Tapiraí municipality in Ribeira Valley, southeastern São Paulo state, Brazil. Influence of climate and landscape factors in Plasmodium infection in Ke. cruzii was addressed. Among the 1719 mosquitoes tested, 3 females collected in Sítio Itapuan and three from Tapiraí were found infected with either P. vivax or P. simium. Results of statistical analyses did not demonstrate association between Plasmodium infection in mosquito and the landscape. Mosquito infection was found in two landscape clusters, with Plasmodium detected in forest fringe mosquitoes. This finding shows that Ke. cruzii can facilitate transmission among human and non-human primates. Plasmodium falciparum was not identified in the samples analyzed. Spatiotemporal variation in local malaria incidence, low prevalence of Plasmodium, variations in humidity and temperature can explain the absence of mosquitoes infected with P. falciparum in the study.
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Affiliation(s)
- B Demari-Silva
- Faculdade de Saúde Pública, Departamento de Epidemiologia. Av. Dr. Arnaldo - 715, São Paulo, SP, CEP 01246-904, Brazil.
| | - G Z Laporta
- Centro Universitário Saúde ABC da Fundação ABC, Setor de Pós-graduação, Pesquisa e Inovação. Av. Lauro Gomes, 2000, Santo André, SP, CEP, 09060-870, Brazil.
| | - Tmp Oliveira
- Faculdade de Saúde Pública, Departamento de Epidemiologia. Av. Dr. Arnaldo - 715, São Paulo, SP, CEP 01246-904, Brazil.
| | - Mam Sallum
- Faculdade de Saúde Pública, Departamento de Epidemiologia. Av. Dr. Arnaldo - 715, São Paulo, SP, CEP 01246-904, Brazil.
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Chan EYY, Ho JY, Hung HHY, Liu S, Lam HCY. Health impact of climate change in cities of middle-income countries: the case of China. Br Med Bull 2019; 130:5-24. [PMID: 31070715 PMCID: PMC6587073 DOI: 10.1093/bmb/ldz011] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 01/31/2019] [Accepted: 04/23/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND This review examines the human health impact of climate change in China. Through reviewing available research findings under four major climate change phenomena, namely extreme temperature, altered rainfall pattern, rise of sea level and extreme weather events, relevant implications for other middle-income population with similar contexts will be synthesized. SOURCES OF DATA Sources of data included bilingual peer-reviewed articles published between 2000 and 2018 in PubMed, Google Scholar and China Academic Journals Full-text Database. AREAS OF AGREEMENT The impact of temperature on mortality outcomes was the most extensively studied, with the strongest cause-specific mortality risks between temperature and cardiovascular and respiratory mortality. The geographical focuses of the studies indicated variations in health risks and impacts of different climate change phenomena across the country. AREAS OF CONTROVERSY While rainfall-related studies predominantly focus on its impact on infectious and vector-borne diseases, consistent associations were not often found. GROWING POINTS Mental health outcomes of climate change had been gaining increasing attention, particularly in the context of extreme weather events. The number of projection studies on the long-term impact had been growing. AREAS TIMELY FOR DEVELOPING RESEARCH The lack of studies on the health implications of rising sea levels and on comorbidity and injury outcomes warrants immediate attention. Evidence is needed to understand health impacts on vulnerable populations living in growing urbanized cities and urban enclaves, in particular migrant workers. Location-specific climate-health outcome thresholds (such as temperature-mortality threshold) will be needed to support evidence-based clinical management plans and health impact mitigation strategies to protect vulnerable communities.
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Affiliation(s)
- Emily Y Y Chan
- Collaborating Centre for Oxford University and CUHK for Disaster and Medical Humanitarian Response (CCOUC), Division of Global Health and Humanitarian Medicine, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
- Division of Global Health and Humanitarian Medicine, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- François-Xavier Bagnoud Center for Health & Human Rights, Harvard University, Boston, MA, USA
| | - Janice Y Ho
- Division of Global Health and Humanitarian Medicine, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Heidi H Y Hung
- Division of Global Health and Humanitarian Medicine, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Sida Liu
- Division of Global Health and Humanitarian Medicine, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Holly C Y Lam
- Collaborating Centre for Oxford University and CUHK for Disaster and Medical Humanitarian Response (CCOUC), Division of Global Health and Humanitarian Medicine, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
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25
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Yi L, Xu X, Ge W, Xue H, Li J, Li D, Wang C, Wu H, Liu X, Zheng D, Chen Z, Liu Q, Bi P, Li J. The impact of climate variability on infectious disease transmission in China: Current knowledge and further directions. ENVIRONMENTAL RESEARCH 2019; 173:255-261. [PMID: 30928856 DOI: 10.1016/j.envres.2019.03.043] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 01/20/2019] [Accepted: 03/17/2019] [Indexed: 05/27/2023]
Abstract
BACKGROUND Climate change may lead to emerging and re-emerging infectious diseases and pose public health challenges to human health and the already overloaded healthcare system. It is therefore important to review current knowledge and identify further directions in China, the largest developing country in the world. METHODS A comprehensive literature review was conducted to examine the relationship between climate variability and infectious disease transmission in China in the new millennium. Literature was identified using the following MeSH terms and keywords: climatic variables [temperature, precipitation, rainfall, humidity, etc.] and infectious disease [viral, bacterial and parasitic diseases]. RESULTS Fifty-eight articles published from January 1, 2000 to May 30, 2018 were included in the final analysis, including bacterial diarrhea, dengue, malaria, Japanese encephalitis, HFRS, HFMD, Schistosomiasis. Each 1 °C rise may lead to 3.6%-14.8% increase in the incidence of bacillary dysentery disease in south China. A 1 °C rise was corresponded to an increase of 1.8%-5.9% in the weekly notified HFMD cases in west China. Each 1 °C rise of temperature, 1% rise in relative humidity and one hour rise in sunshine led to an increase of 0.90%, 3.99% and 0.68% in the monthly malaria cases, respectively. Climate change with the increased temperature and irregular patterns of rainfall may affect the pathogen reproduction rate, their spread and geographical distribution, change human behavior and influence the ecology of vectors, and increase the rate of disease transmission in different regions of China. CONCLUSION Exploring relevant adaptation strategies and the health burden of climate change will assist public health authorities to develop an early warning system and protect China's population health, especially in the new 1.5 °C scenario of the newly released IPCC special report.
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Affiliation(s)
- Liping Yi
- Division of Environmental Health, School of Public Health and Management, Weifang Medical University, Weifang, 261053, Shandong Province, PR China
| | - Xin Xu
- Department of Dentistry, Affiliated Hospital, Weifang Medical University, Weifang, 261053, Shandong Province, PR China
| | - Wenxin Ge
- Division of Environmental Health, School of Public Health and Management, Weifang Medical University, Weifang, 261053, Shandong Province, PR China
| | - Haibin Xue
- Clinical Laboratory, Weifang People's Hospital, Weifang, 261000. Shandong Province, PR China
| | - Jin Li
- Department of Dentistry, Weifang People's Hospital, Weifang, 261000, Shandong Province, PR China
| | - Daoyuan Li
- Department of Emergency, Weifang No.2 People's Hospital, Weifang, 261041, Shandong Province, PR China
| | - Chunping Wang
- Division of Environmental Health, School of Public Health and Management, Weifang Medical University, Weifang, 261053, Shandong Province, PR China
| | - Haixia Wu
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, PR China
| | - Xiaobo Liu
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, PR China
| | - Dashan Zheng
- Division of Environmental Health, School of Public Health and Management, Weifang Medical University, Weifang, 261053, Shandong Province, PR China
| | - Zhe Chen
- Division of Environmental Health, School of Public Health and Management, Weifang Medical University, Weifang, 261053, Shandong Province, PR China
| | - Qiyong Liu
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, PR China
| | - Peng Bi
- School of Public Health, The University of Adelaide, Adelaide, SA 5005, Australia; School of Public Health, Anhui Medical University, Hefei, 230032, Anhui Province, PR China.
| | - Jing Li
- Division of Environmental Health, School of Public Health and Management, Weifang Medical University, Weifang, 261053, Shandong Province, PR China; "Health Shandong" Major Social Risk Prediction and Governance Collaborative Innovation Center, Weifang, 261053, Shandong Province, PR China.
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26
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Mfuh KO, Achonduh-Atijegbe OA, Bekindaka ON, Esemu LF, Mbakop CD, Gandhi K, Leke RGF, Taylor DW, Nerurkar VR. A comparison of thick-film microscopy, rapid diagnostic test, and polymerase chain reaction for accurate diagnosis of Plasmodium falciparum malaria. Malar J 2019; 18:73. [PMID: 30866947 PMCID: PMC6416847 DOI: 10.1186/s12936-019-2711-4] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 03/06/2019] [Indexed: 11/18/2022] Open
Abstract
Background Accurate diagnosis of malaria is important for effective disease management and control. In Cameroon, presumptive clinical diagnosis, thick-film microscopy (TFM), and rapid diagnostic tests (RDT) are commonly used to diagnose cases of Plasmodium falciparum malaria. However, these methods lack sensitivity to detect low parasitaemia. Polymerase chain reaction (PCR), on the other hand, enhances the detection of sub-microscopic parasitaemia making it a much-needed tool for epidemiological surveys, mass screening, and the assessment of interventions for malaria elimination. Therefore, this study sought to determine the frequency of cases missed by traditional methods that are detected by PCR. Methods Blood samples, collected from 551 febrile Cameroonian patients between February 2014 and February 2015, were tested for P. falciparum by microscopy, RDT and PCR. The hospital records of participants were reviewed to obtain data on the clinical diagnosis made by the health care worker. Results The prevalence of malaria by microscopy, RDT and PCR was 31%, 45%, and 54%, respectively. However, of the 92% of participants diagnosed as having clinical cases of malaria by the health care worker, 38% were malaria-negative by PCR. PCR detected 23% and 12% more malaria infections than microscopy and RDT, respectively. A total of 128 (23%) individuals had sub-microscopic infections in the study population. The sensitivity of microscopy, RDT, and clinical diagnosis was 57%, 78% and 100%; the specificity was 99%, 94%, and 17%; the positive predictive values were 99%, 94%, and 59%; the negative predictive values were 66%, 78%, and 100%, respectively. Thus, 41% of the participants clinically diagnosed as having malaria had fever caused by other pathogens. Conclusions Malaria diagnostic methods, such as TFM and RDT missed 12–23% of malaria cases detected by PCR. Therefore, traditional diagnostic approaches (TFM, RDT and clinical diagnosis) are not adequate when accurate epidemiological data are needed for monitoring malaria control and elimination interventions.
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Affiliation(s)
- Kenji O Mfuh
- Department of Tropical Medicine, Medical Microbiology and Pharmacology, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA.,Biotechnology Center, University of Yaoundé I, Yaoundé, Cameroon
| | | | | | - Livo F Esemu
- Biotechnology Center, University of Yaoundé I, Yaoundé, Cameroon
| | - Calixt D Mbakop
- National Medical Research Institute (IMPM), Yaoundé, Cameroon
| | - Krupa Gandhi
- Biostatistics Core Facility Department of Complementary & Integrative Medicine, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Rose G F Leke
- Biotechnology Center, University of Yaoundé I, Yaoundé, Cameroon
| | - Diane W Taylor
- Department of Tropical Medicine, Medical Microbiology and Pharmacology, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Vivek R Nerurkar
- Department of Tropical Medicine, Medical Microbiology and Pharmacology, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA. .,Pacific Center for Emerging Infectious Diseases Research, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA.
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Deku JG, Lokpo SY, Kye-Amoah KK, Orish VN, Ussher FA, Esson J, Aduko RA, Dakorah MP, Osei-Yeboah J. Malaria Burden and Trend Among Clients Seeking Healthcare in the Western Region: A 4-Year Retrospective Study at the Sefwi-Wiawso Municipal Hospital, Ghana. Open Microbiol J 2018. [DOI: 10.2174/1874285801812010404] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Background:
Malaria cases continue to rise despite sustained efforts directed at eliminating the burden among Ghanaians. This study was aimed at describing the spectrum of malaria burden in a four-year (2013-2016) retrospective review among clients seeking care at the Sefwi-Wiawso Municipal Hospital in the Western Region of Ghana.
Materials and Methods:
The study analyzed secondary data extracted on 32,629 patients who were referred to the Laboratory for malaria testing from January 2013 to December 2016. Socio-demographic data included age and gender, department of test requisition and malaria results were obtained from the archived Daily Malaria Logbook records. Approval for the study was granted by the authorities of the Sefwi-Wiawso Municipal Hospital.
Results:
The overall confirmed malaria case was 8629 (26.5%), among under five 1,384 (58.7%), pregnant women 4451 (20.3%) and 14.1% among asymptomatic population. Significant gender disparity in the confirmation of suspected malaria cases was observed with males recording higher rate (45.8%) than females (36.7%). The peak of the malaria epidemic was observed in the wet season (195 cases per month), compared to the dry season (133 cases per month).
Conclusion:
Cases of malaria is increasing with high rates among vulnerable groups in the Western Region. There is the need to intensify efforts to reduce the burden in the study area especially among vulnerable groups.
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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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Babaie J, Barati M, Azizi M, Ephtekhari A, Sadat SJ. A systematic evidence review of the effect of climate change on malaria in Iran. J Parasit Dis 2018; 42:331-340. [PMID: 30166779 PMCID: PMC6104236 DOI: 10.1007/s12639-018-1017-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Accepted: 07/03/2018] [Indexed: 11/26/2022] Open
Abstract
Climate is an effective factor in the ecological structure which plays an important role in control and outbreak of the diseases caused by biological factors like malaria. With regard to the occurring climatic change, this study aimed to review the effects of climate change on malaria in Iran. In this systematic review, Cochrane, PubMed and ScienceDirect (as international databases), SID and Magiran as Persian databases were investigated through MESH keywords including climate change, global warming, malaria, Anopheles, and Iran. The related articles were screened and finally their results were extracted using data extraction sheets. Totally 41 papers were resulted through databases searching process. Finally 14 papers which met inclusion criteria were included in data extraction stage. The findings indicated that Anopheles mosquitoes are present at least in 115 places in Iran; they are compatible with climatic zones of Iran. Malaria and it's vectors are affected by climate change. Temperature, precipitation, relative humidity, wind intensity and direction are the most important climatic factors affecting the growth and proliferation of Anopheles, Plasmodium and the prevalence of malaria. The transmission of malaria in Iran is associated with the climatic factors of temperature, rainfall, and humidity. Therefore, with regard to the occurring climatic change, the incidence of the disease may also change which needs to be taken into consideration while planning of malaria control.
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Affiliation(s)
- Javad Babaie
- Iranian Center of Excellence in Health Management, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mohammad Barati
- Infectious Diseases Research Center, Aja University of Medical Sciences, Tehran, Iran
| | - Maryam Azizi
- Department of Health in Disaster and Emergency, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Adel Ephtekhari
- Department of Health in Disaster and Emergency, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Seyed Javad Sadat
- Department of Health in Disaster and Emergency, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
- Social Determinants of Health Research Center, Yasuj University of Medical Sciences, Yasuj, Iran
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30
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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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A Time Series Analysis: Weather Factors, Human Migration and Malaria Cases in Endemic Area of Purworejo, Indonesia, 2005-2014. IRANIAN JOURNAL OF PUBLIC HEALTH 2018; 47:499-509. [PMID: 29900134 PMCID: PMC5996329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
BACKGROUND Climatic and weather factors become important determinants of vector-borne diseases transmission like malaria. This study aimed to prove relationships between weather factors with considering human migration and previous case findings and malaria cases in endemic areas in Purworejo during 2005-2014. METHODS This study employed ecological time series analysis by using monthly data. The independent variables were the maximum temperature, minimum temperature, maximum humidity, minimum humidity, precipitation, human migration, and previous malaria cases, while the dependent variable was positive malaria cases. Three models of count data regression analysis i.e. Poisson model, quasi-Poisson model, and negative binomial model were applied to measure the relationship. The least Akaike Information Criteria (AIC) value was also performed to find the best model. Negative binomial regression analysis was considered as the best model. RESULTS The model showed that humidity (lag 2), precipitation (lag 3), precipitation (lag 12), migration (lag1) and previous malaria cases (lag 12) had a significant relationship with malaria cases. CONCLUSION Weather, migration and previous malaria cases factors need to be considered as prominent indicators for the increase of malaria case projection.
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32
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Association between malaria incidence and meteorological factors: a multi-location study in China, 2005-2012. Epidemiol Infect 2017; 146:89-99. [PMID: 29248024 DOI: 10.1017/s0950268817002254] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
This study aims to investigate the climate-malaria associations in nine cities selected from malaria high-risk areas in China. Daily reports of malaria cases in Anhui, Henan, and Yunnan Provinces for 2005-2012 were obtained from the Chinese Center for Disease Control and Prevention. Generalized estimating equation models were used to quantify the city-specific climate-malaria associations. Multivariate random-effects meta-regression analyses were used to pool the city-specific effects. An inverted-U-shaped curve relationship was observed between temperatures, average relative humidity, and malaria. A 1 °C increase of maximum temperature (T max) resulted in 6·7% (95% CI 4·6-8·8%) to 15·8% (95% CI 14·1-17·4%) increase of malaria, with corresponding lags ranging from 7 to 45 days. For minimum temperature (T min), the effect estimates peaked at lag 0 to 40 days, ranging from 5·3% (95% CI 4·4-6·2%) to 17·9% (95% CI 15·6-20·1%). Malaria is more sensitive to T min in cool climates and T max in warm climates. The duration of lag effect in a cool climate zone is longer than that in a warm climate zone. Lagged effects did not vanish after an epidemic season but waned gradually in the following 2-3 warm seasons. A warming climate may potentially increase the risk of malaria resurgence in China.
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Sissoko MS, Sissoko K, Kamate B, Samake Y, Goita S, Dabo A, Yena M, Dessay N, Piarroux R, Doumbo OK, Gaudart J. Temporal dynamic of malaria in a suburban area along the Niger River. Malar J 2017; 16:420. [PMID: 29058578 PMCID: PMC5651586 DOI: 10.1186/s12936-017-2068-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Accepted: 10/16/2017] [Indexed: 12/05/2022] Open
Abstract
Background Even if rainfall and temperature are factors classically associated to malaria, little is known about other meteorological factors, their variability and combinations related to malaria, in association with river height variations. Furthermore, in suburban area, urbanization and growing population density should be assessed in relation to these environmental factors. The aim of this study was to assess the impact of combined environmental, meteorological and hydrological factors on malaria incidence through time in the context of urbanization. Methods Population observational data were prospectively collected. Clinical malaria was defined as the presence of parasites in addition to clinical symptoms. Meteorological and hydrological factors were measured daily. For each factors variation indices were estimated. Urbanization was yearly estimated assessing satellite imaging and field investigations. Principal component analysis was used for dimension reduction and factors combination. Lags between malaria incidences and the main components were assessed by cross-correlation functions. Generalized additive model was used to assess relative impact of different environmental components, taking into account lags, and modelling non-linear relationships. Change-point analysis was used to determine transmission periods within years. Results Malaria incidences were dominated by annual periodicity and varied through time without modification of the dynamic, with no impact of the urbanization. The main meteorological factor associated with malaria was a combination of evaporation, humidity and rainfall, with a lag of 3 months. The relationship between combined temperature factors showed a linear impact until reaching high temperatures limiting malaria incidence, with a lag 3.25 months. Height and variation of the river were related to malaria incidence (respectively 6 week lag and no lag). Conclusions The study emphasizes no decreasing trend of malaria incidence despite accurate access to care and control strategies in accordance to international recommendations. Furthermore, no decreasing trend was showed despite the urbanization of the area. Malaria transmission remain increase 3 months after the beginning of the dry season. Addition to evaporation versus humidity/rainfall, nonlinear relationship for temperature and river height and variations have to be taken into account when implementing malaria control programmes.
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Affiliation(s)
- Mahamadou Soumana Sissoko
- Department of Epidemiology of Parasitic Diseases-Faculty of Medicine and Dentistry-Faculty of Pharmacy, Malaria Research and Training Centre, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali. .,Aix Marseille Univ, IRD, INSERM, SESSTIM, 13005, Marseille, France.
| | - Kourane Sissoko
- Department of Epidemiology of Parasitic Diseases-Faculty of Medicine and Dentistry-Faculty of Pharmacy, Malaria Research and Training Centre, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Bourama Kamate
- Department of Epidemiology of Parasitic Diseases-Faculty of Medicine and Dentistry-Faculty of Pharmacy, Malaria Research and Training Centre, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Yacouba Samake
- Department of Epidemiology of Parasitic Diseases-Faculty of Medicine and Dentistry-Faculty of Pharmacy, Malaria Research and Training Centre, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Siaka Goita
- Department of Epidemiology of Parasitic Diseases-Faculty of Medicine and Dentistry-Faculty of Pharmacy, Malaria Research and Training Centre, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Abdoulaye Dabo
- Department of Epidemiology of Parasitic Diseases-Faculty of Medicine and Dentistry-Faculty of Pharmacy, Malaria Research and Training Centre, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Mama Yena
- Direction Nationale de l'Hydraulique, Bamako, Mali
| | - Nadine Dessay
- Nadine Dessay, UMR 228 ESPACE-DEV (IRD, UM, UG, UA, UR), Responsable équipe Observation Spatiale de l'Environnement (OSE), Maison de la Télédétection, 500 rue Jean-François Breton, 34093, Montpellier Cedex 5, France
| | - Renaud Piarroux
- Service de Parasitologie-Mycologie Hôpital de la Timone et UMR MD 3 Aix-Marseille Université, Marseille, France
| | - Ogobara K Doumbo
- Department of Epidemiology of Parasitic Diseases-Faculty of Medicine and Dentistry-Faculty of Pharmacy, Malaria Research and Training Centre, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali.,Aix Marseille Univ, IRD, INSERM, SESSTIM, 13005, Marseille, France
| | - Jean Gaudart
- Aix Marseille Univ, IRD, INSERM, SESSTIM, 13005, Marseille, France
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Huang Q, Hu L, Liao QB, Xia J, Wang QR, Peng HJ. Spatiotemporal Analysis of the Malaria Epidemic in Mainland China, 2004-2014. Am J Trop Med Hyg 2017; 97:504-513. [PMID: 28829728 DOI: 10.4269/ajtmh.16-0711] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
The purpose of this study is to characterize spatiotemporal heterogeneities in malaria distribution at a provincial level and investigate the association between malaria incidence and climate factors from 2004 to 2014 in China to inform current malaria control efforts. National malaria incidence peaked (4.6/100,000) in 2006 and decreased to a very low level (0.21/100,000) in 2014, and the proportion of imported cases increased from 16.2% in 2004 to 98.2% in 2014. Statistical analyses of global and local spatial autocorrelations and purely spatial scan statistics revealed that malaria was localized in Hainan, Anhui, and Yunnan during 2004-2009 and then gradually shifted and clustered in Yunnan after 2010. Purely temporal clusters shortened to less than 5 months during 2012-2014. The two most likely clusters detected using spatiotemporal analysis occurred in Anhui between July 2005 and November 2007 and Yunnan between January 2010 and June 2012. Correlation coefficients for the association between malaria incidence and climate factors sharply decreased after 2010, and there were zero-month lag effects for climate factors during 2010-2014. Overall, the spatiotemporal distribution of malaria in China changed from relatively scattered (2004-2009) to relatively clustered (2010-2014). As the proportion of imported cases increased, the effect of climate factors on malaria incidence has gradually become weaker since 2011. Therefore, new warning systems should be applied to monitor resurgence and outbreaks of malaria in mainland China, and quarantine at borders should be reinforced to control the increasingly trend of imported malaria cases.
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Affiliation(s)
- Qiang Huang
- Department of Pathogen Biology, Guangdong Provincial Key Laboratory of Tropical Disease Research, and Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, School of Public Health, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Lin Hu
- Department of Pathogen Biology, Guangdong Provincial Key Laboratory of Tropical Disease Research, and Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, School of Public Health, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Qi-Bin Liao
- Department of Pathogen Biology, Guangdong Provincial Key Laboratory of Tropical Disease Research, and Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, School of Public Health, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Jing Xia
- Department of Pathogen Biology, Guangdong Provincial Key Laboratory of Tropical Disease Research, and Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, School of Public Health, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Qian-Ru Wang
- Department of Atmospheric Science, College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu, Sichuan, China
| | - Hong-Juan Peng
- Department of Pathogen Biology, Guangdong Provincial Key Laboratory of Tropical Disease Research, and Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, School of Public Health, Southern Medical University, Guangzhou, Guangdong Province, China
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Kumar R, Dash C, Rani K. Ecological covariates based predictive model of malaria risk in the state of Chhattisgarh, India. J Parasit Dis 2017; 41:761-767. [PMID: 28848275 DOI: 10.1007/s12639-017-0885-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2015] [Accepted: 01/27/2017] [Indexed: 11/28/2022] Open
Abstract
Malaria being an endemic disease in the state of Chhattisgarh and ecologically dependent mosquito-borne disease, the study is intended to identify the ecological covariates of malaria risk in districts of the state and to build a suitable predictive model based on those predictors which could assist developing a weather based early warning system. This secondary data based analysis used one month lagged district level malaria positive cases as response variable and ecological covariates as independent variables which were tested with fixed effect panelled negative binomial regression models. Interactions among the covariates were explored using two way factorial interaction in the model. Although malaria risk in the state possesses perennial characteristics, higher parasitic incidence was observed during the rainy and winter seasons. The univariate analysis indicated that the malaria incidence risk was statistically significant associated with rainfall, maximum humidity, minimum temperature, wind speed, and forest cover (p < 0.05). The efficient predictive model include the forest cover [IRR-1.033 (1.024-1.042)], maximum humidity [IRR-1.016 (1.013-1.018)], and two-way factorial interactions between district specific averaged monthly minimum temperature and monthly minimum temperature, monthly minimum temperature was statistically significant [IRR-1.44 (1.231-1.695)] whereas the interaction term has a protective effect [IRR-0.982 (0.974-0.990)] against malaria infections. Forest cover, maximum humidity, minimum temperature and wind speed emerged as potential covariates to be used in predictive models for modelling the malaria risk in the state which could be efficiently used for early warning systems in the state.
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Affiliation(s)
- Rajesh Kumar
- Child Right and You (CRY), Sayad Ul Ajab, Westend Marg, New Delhi, 110030 India
| | | | - Khushbu Rani
- Women and Child Welfare Consultant, New Delhi, India
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Qin HY, Xiao JH, Li JX, Gao X, Wang HB. Climate Variability and Avian Cholera Transmission in Guangxi, China. BRAZILIAN JOURNAL OF POULTRY SCIENCE 2017. [DOI: 10.1590/1806-9061-2016-0411] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Affiliation(s)
- HY Qin
- Northeast Agricultural University, China
| | - JH Xiao
- Northeast Agricultural University, China
| | - JX Li
- Northeast Agricultural University, China
| | - X Gao
- Northeast Agricultural University, China
| | - HB Wang
- Northeast Agricultural University, China
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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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38
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A Weather-Based Prediction Model of Malaria Prevalence in Amenfi West District, Ghana. Malar Res Treat 2017; 2017:7820454. [PMID: 28255497 PMCID: PMC5307250 DOI: 10.1155/2017/7820454] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Accepted: 01/15/2017] [Indexed: 01/07/2023] Open
Abstract
This study investigated the effects of climatic variables, particularly, rainfall and temperature, on malaria incidence using time series analysis. Our preliminary analysis revealed that malaria incidence in the study area decreased at about 0.35% annually. Also, the month of November recorded approximately 21% more malaria cases than the other months while September had a decreased effect of about 14%. The forecast model developed for this investigation indicated that mean minimum (P = 0.01928) and maximum (P = 0.00321) monthly temperatures lagged at three months were significant predictors of malaria incidence while rainfall was not. Diagnostic tests using Ljung-Box and ARCH-LM tests revealed that the model developed was adequate for forecasting. Forecast values for 2016 to 2020 generated by our model suggest a possible future decline in malaria incidence. This goes to suggest that intervention strategies put in place by some nongovernmental and governmental agencies to combat the disease are effective and thus should be encouraged and routinely monitored to yield more desirable outcomes.
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Wu Y, Qiao Z, Wang N, Yu H, Feng Z, Li X, Zhao X. Describing interaction effect between lagged rainfalls on malaria: an epidemiological study in south-west China. Malar J 2017; 16:53. [PMID: 28137250 PMCID: PMC5282846 DOI: 10.1186/s12936-017-1706-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Accepted: 01/20/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND When discussing the relationship between meteorological factors and malaria, previous studies mainly focus on the interaction between different climatic factors, while the possible interaction within one particular climatic predictor at different lag periods has been largely neglected. In this study, this issue was investigated by exploring the interaction of lagged rainfalls and its impact on malaria epidemics, which is a typical example of those meteorological variables. METHODS The weekly data of malaria cases and three climatic variables of 30 counties in southwest China from 2004 to 2009 were analysed with the varying coefficient-distributed lag non-linear model. The correlation patterns of the 6th, 9th and 12th week lags would vary over different rainfall levels at the 4th-week lag. RESULTS The non-linear patterns for rainfall at different rainfall levels are distinct from each other. In the low rainfall level at the 4th week lag, the increasing rainfall may promote the transmission of malaria. However, for the high rainfall level at the 4th week lag, evidence shows that the excessive rainfall decreases the risk of malaria. CONCLUSION This study reports for the first time that the interaction effect between lagged rainfalls on malaria exists, and highlights the importance of integrating the interaction between lagged predictors in relevant studies, which could help to better understand and predict malaria transmission.
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Affiliation(s)
- Yunyun Wu
- West China School of Public Health, Sichuan University, No. 17 Section 3, South Renmin Road, 610041, Chengdu, China
| | - Zhijiao Qiao
- West China School of Public Health, Sichuan University, No. 17 Section 3, South Renmin Road, 610041, Chengdu, China
| | - Nan Wang
- West China School of Public Health, Sichuan University, No. 17 Section 3, South Renmin Road, 610041, Chengdu, China
| | - Hongjie Yu
- West China School of Public Health, Sichuan University, No. 17 Section 3, South Renmin Road, 610041, Chengdu, China.,Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zijian Feng
- Office for Disease Control and Emergency Response, Chinese Centre for Disease Control and Prevention, NE Pacific Street, 102206, Beijing, China
| | - Xiaosong Li
- West China School of Public Health, Sichuan University, No. 17 Section 3, South Renmin Road, 610041, Chengdu, China
| | - Xing Zhao
- West China School of Public Health, Sichuan University, No. 17 Section 3, South Renmin Road, 610041, Chengdu, China.
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Jing Y, Wang X, Tang S, Wu J. Data informed analysis of 2014 dengue fever outbreak in Guangzhou: Impact of multiple environmental factors and vector control. J Theor Biol 2016; 416:161-179. [PMID: 28039013 DOI: 10.1016/j.jtbi.2016.12.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Revised: 12/13/2016] [Accepted: 12/17/2016] [Indexed: 10/20/2022]
Abstract
Epidemics of dengue fever in China were reported before 1940 and the outbreak of dengue fever in Guangdong province in 2014 is the most serious so far. The important question is what factors account for this serious outbreak, and how to evaluate the sensitivity of the multiple factors including weather variables and human actions on the dengue disease. Therefore, according to the relations among the temperature (daily mean temperature (DMT) and diurnal temperature range (DTR)), vector parameters and reproduction number we have proposed the analytical formula for the relative vector's capacity and effective reproduction number, and then we have the formula for the likelihood function by employing the generation interval-informed method. This allows us to estimate the unknown vector parameters by the maximum likelihood method and carry out the sensitivity analysis. The correlations between the density of mosquito vectors (the Breteau index (BI), the adult mosquito density) and the daily newly reported cases of four different districts of Guangzhou city have been studied by using the Pearson correlation and cross-correlation analyses. Our findings indicate that both the BI and the adult mosquito density are statistically significantly correlated with the daily newly reported cases, and the vector parameters are closely related to the DMT and DTR with relative complex relationships, which influence the effective reproduction number comprehensively. Moreover, the trend of the effective reproduction number is consistent with daily newly reported cases, which confirms the effectiveness of the government control measures. Sensitivity analysis results indicate that the temperature can be either an effective barrier or a facilitator of vector-borne diseases, and consequently weather variable may result in complexity of dengue disease control.
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Affiliation(s)
- Yi Jing
- School of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, PR China
| | - Xia Wang
- School of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, PR China.
| | - Sanyi Tang
- School of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, PR China.
| | - Jianhong Wu
- Centre for Disease Modelling, York Institute for Health Research, York University, Toronto, Ontario, Canada
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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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The relation between climatic factors and malaria incidence in Kerman, South East of Iran. Parasite Epidemiol Control 2016; 1:205-210. [PMID: 29988199 PMCID: PMC5991842 DOI: 10.1016/j.parepi.2016.06.001] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Revised: 06/11/2016] [Accepted: 06/11/2016] [Indexed: 11/28/2022] Open
Abstract
Background and objectives Malaria is among the most important parasitic diseases, and is one of the endemic diseases in Iran. This disease is often known as a disease related to climate changes. Due to the health and economic burden of malaria and the location of Kerman province in an area with high incidence of malaria, the present study aimed to evaluate the effects of climatic factors on the incidence of this disease. Material and methods Data on the incidence of malaria in Kerman province was inquired from Kerman and Jiroft Medical Universities and climatic variables were inquired from the meteorological organization of Kerman. The data was analyzed monthly from 2000 to 2012. Variations in incidence of malaria with climatic factors were assessed with negative binomial regression model in STATA11software. In order to determine the delayed effects of meteorological variables on malaria incidence, cross-correlation analysis was done with Minitab16. Results The most effective meteorological factor on the incidence of malaria was temperature. As the mean, maximum, and minimum of monthly temperature increased, the incidence rate raised significantly. The multivariate negative binomial regression model indicates that a 1 °C increase in maximum temperature in a given month was related to a 15% and 19% increase on malaria incidence on the same and subsequent month, respectively (p-value = 0.001). Humidity and Rainfall were not significant in the adjusted model. Conclusion Temperature is among the effective climatic parameters on the incidence of malaria which should be considered in planning for control and prevention of the disease.
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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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Srimath-Tirumula-Peddinti RCPK, Neelapu NRR, Sidagam N. Association of Climatic Variability, Vector Population and Malarial Disease in District of Visakhapatnam, India: A Modeling and Prediction Analysis. PLoS One 2015; 10:e0128377. [PMID: 26110279 PMCID: PMC4482491 DOI: 10.1371/journal.pone.0128377] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2014] [Accepted: 04/26/2015] [Indexed: 01/02/2023] Open
Abstract
Background Malarial incidence, severity, dynamics and distribution of malaria are strongly determined by climatic factors, i.e., temperature, precipitation, and relative humidity. The objectives of the current study were to analyse and model the relationships among climate, vector and malaria disease in district of Visakhapatnam, India to understand malaria transmission mechanism (MTM). Methodology Epidemiological, vector and climate data were analysed for the years 2005 to 2011 in Visakhapatnam to understand the magnitude, trends and seasonal patterns of the malarial disease. Statistical software MINITAB ver. 14 was used for performing correlation, linear and multiple regression analysis. Results/Findings Perennial malaria disease incidence and mosquito population was observed in the district of Visakhapatnam with peaks in seasons. All the climatic variables have a significant influence on disease incidence as well as on mosquito populations. Correlation coefficient analysis, seasonal index and seasonal analysis demonstrated significant relationships among climatic factors, mosquito population and malaria disease incidence in the district of Visakhapatnam, India. Multiple regression and ARIMA (I) models are best suited models for modeling and prediction of disease incidences and mosquito population. Predicted values of average temperature, mosquito population and malarial cases increased along with the year. Developed MTM algorithm observed a major MTM cycle following the June to August rains and occurring between June to September and minor MTM cycles following March to April rains and occurring between March to April in the district of Visakhapatnam. Fluctuations in climatic factors favored an increase in mosquito populations and thereby increasing the number of malarial cases. Rainfall, temperatures (20°C to 33°C) and humidity (66% to 81%) maintained a warmer, wetter climate for mosquito growth, parasite development and malaria transmission. Conclusions/Significance Changes in climatic factors influence malaria directly by modifying the behaviour and geographical distribution of vectors and by changing the length of the life cycle of the parasite.
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Affiliation(s)
| | - Nageswara Rao Reddy Neelapu
- Department of Biochemistry and Bioinformatics, GITAM Institute of Science, GITAM University, Rushikonda Campus, Visakhapatnam, Andhra Pradesh, India
| | - Naresh Sidagam
- Department of Statistics, College of Science and Technology, Andhra University, Waltair, Visakhapatnam, Andhra Pradesh, India
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Upadhyayula SM, Mutheneni SR, Chenna S, Parasaram V, Kadiri MR. Climate drivers on malaria transmission in Arunachal Pradesh, India. PLoS One 2015; 10:e0119514. [PMID: 25803481 PMCID: PMC4372434 DOI: 10.1371/journal.pone.0119514] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2014] [Accepted: 01/23/2015] [Indexed: 01/19/2023] Open
Abstract
The present study was conducted during the years 2006 to 2012 and provides information on prevalence of malaria and its regulation with effect to various climatic factors in East Siang district of Arunachal Pradesh, India. Correlation analysis, Principal Component Analysis and Hotelling's T² statistics models are adopted to understand the effect of weather variables on malaria transmission. The epidemiological study shows that the prevalence of malaria is mostly caused by the parasite Plasmodium vivax followed by Plasmodium falciparum. It is noted that, the intensity of malaria cases declined gradually from the year 2006 to 2012. The transmission of malaria observed was more during the rainy season, as compared to summer and winter seasons. Further, the data analysis study with Principal Component Analysis and Hotelling's T² statistic has revealed that the climatic variables such as temperature and rainfall are the most influencing factors for the high rate of malaria transmission in East Siang district of Arunachal Pradesh.
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Affiliation(s)
- Suryanaryana Murty Upadhyayula
- Biology Division, Council of Scientific and Industrial Research-Indian Institute of Chemical Technology, Hyderabad-500 607, India
| | - Srinivasa Rao Mutheneni
- Biology Division, Council of Scientific and Industrial Research-Indian Institute of Chemical Technology, Hyderabad-500 607, India
| | - Sumana Chenna
- Chemical Engineering Sciences, Council of Scientific and Industrial Research-Indian Institute of Chemical Technology, Hyderabad-500 607, India
| | - Vaideesh Parasaram
- Chemical Engineering Sciences, Council of Scientific and Industrial Research-Indian Institute of Chemical Technology, Hyderabad-500 607, India
| | - Madhusudhan Rao Kadiri
- Biology Division, Council of Scientific and Industrial Research-Indian Institute of Chemical Technology, Hyderabad-500 607, India
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Guo C, Yang L, Ou CQ, Li L, Zhuang Y, Yang J, Zhou YX, Qian J, Chen PY, Liu QY. Malaria incidence from 2005-2013 and its associations with meteorological factors in Guangdong, China. Malar J 2015; 14:116. [PMID: 25881185 PMCID: PMC4389306 DOI: 10.1186/s12936-015-0630-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2014] [Accepted: 03/01/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The temporal variation of malaria incidence has been linked to meteorological factors in many studies, but key factors observed and corresponding effect estimates were not consistent. Furthermore, the potential effect modification by individual characteristics is not well documented. This study intends to examine the delayed effects of meteorological factors and the sub-population's susceptibility in Guangdong, China. METHODS The Granger causality Wald test and Spearman correlation analysis were employed to select climatic variables influencing malaria. The distributed lag non-linear model (DLNM) was used to estimate the non-linear and delayed effects of weekly temperature, duration of sunshine, and precipitation on the weekly number of malaria cases after controlling for other confounders. Stratified analyses were conducted to identify the sub-population's susceptibility to meteorological effects by malaria type, gender, and age group. RESULTS An incidence rate of 1.1 cases per 1,000,000 people was detected in Guangdong from 2005-2013. High temperature was associated with an observed increase in malaria incidence, with the effect lasting for four weeks and a maximum relative risk (RR) of 1.57 (95% confidence interval (CI): 1.06-2.33) by comparing 30°C to the median temperature. The effect of sunshine duration peaked at lag five and the maximum RR was 1.36 (95% CI: 1.08-1.72) by comparing 24 hours/week to 0 hours/week. A J-shaped relationship was found between malaria incidence and precipitation with a threshold of 150 mm/week. Over the threshold, precipitation increased malaria incidence after four weeks with the effect lasting for 15 weeks, and the maximum RR of 1.55 (95% CI: 1.18-2.03) occurring at lag eight by comparing 225 mm/week to 0 mm/week. Plasmodium falciparum was more sensitive to temperature and precipitation than Plasmodium vivax. Females had a higher susceptibility to the effects of sunshine and precipitation, and children and the elderly were more sensitive to the change of temperature, sunshine duration, and precipitation. CONCLUSION Temperature, duration of sunshine and precipitation played important roles in malaria incidence with effects delayed and varied across lags. Climatic effects were distinct among sub-groups. This study provided helpful information for predicting malaria incidence and developing the future warning system.
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Affiliation(s)
- Cui Guo
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, School of Public Health and Tropical Medicine, Southern Medical University, Guangzhou, 510515, China.
| | - Lin Yang
- Department of Nursing, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong, China.
| | - Chun-Quan Ou
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, School of Public Health and Tropical Medicine, Southern Medical University, Guangzhou, 510515, China.
| | - Li Li
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, School of Public Health and Tropical Medicine, Southern Medical University, Guangzhou, 510515, China.
| | - Yan Zhuang
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, School of Public Health and Tropical Medicine, Southern Medical University, Guangzhou, 510515, China.
| | - Jun Yang
- State Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
| | - Ying-Xue Zhou
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, School of Public Health and Tropical Medicine, Southern Medical University, Guangzhou, 510515, China.
| | - Jun Qian
- Department of Mathematics and Physics, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
| | - Ping-Yan Chen
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, School of Public Health and Tropical Medicine, Southern Medical University, Guangzhou, 510515, China.
| | - Qi-Yong Liu
- State Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
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Komen K, Olwoch J, Rautenbach H, Botai J, Adebayo A. Long-run relative importance of temperature as the main driver to malaria transmission in Limpopo Province, South Africa: a simple econometric approach. ECOHEALTH 2015; 12:131-43. [PMID: 25515074 DOI: 10.1007/s10393-014-0992-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2012] [Revised: 10/10/2014] [Accepted: 10/13/2014] [Indexed: 05/15/2023]
Abstract
Malaria in Limpopo Province of South Africa is shifting and now observed in originally non-malaria districts, and it is unclear whether climate change drives this shift. This study examines the distribution of malaria at district level in the province, determines direction and strength of the linear relationship and causality between malaria with the meteorological variables (rainfall and temperature) and ascertains their short- and long-run variations. Spatio-temporal method, Correlation analysis and econometric methods are applied. Time series monthly meteorological data (1998-2007) were obtained from South Africa Weather Services, while clinical malaria data came from Malaria Control Centre in Tzaneen (Limpopo Province) and South African Department of Health. We find that malaria changes and pressures vary in different districts with a strong positive correlation between temperature with malaria, r = 0.5212, and a weak positive relationship for rainfall, r = 0.2810. Strong unidirectional causality runs from rainfall and temperature to malaria cases (and not vice versa): F (1, 117) = 3.89, ρ = 0.0232 and F (1, 117) = 20.08, P < 0.001 and between rainfall and temperature, a bi-directional causality exists: F (1, 117) = 19.80; F (1,117) = 17.14, P < 0.001, respectively, meaning that rainfall affects temperature and vice versa. Results show evidence of strong existence of a long-run relationship between climate variables and malaria, with temperature maintaining very high level of significance than rainfall. Temperature, therefore, is more important in influencing malaria transmission in Limpopo Province.
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Affiliation(s)
- Kibii Komen
- Geoinformatics and Meteorology - Center for Environmental Studies, Department of Geography, University of Pretoria, Pretoria, 0002, South Africa,
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Feng XY, Xia ZG, Vong S, Yang WZ, Zhou SS. Surveillance and response to drive the national malaria elimination program. ADVANCES IN PARASITOLOGY 2015; 86:81-108. [PMID: 25476882 DOI: 10.1016/b978-0-12-800869-0.00004-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The national action plan for malaria elimination in China (2010-2020) was issued by the Chinese Ministry of Health along with other 13 ministries and commissions in 2010. The ultimate goal of the national action plan was to eliminate local transmission of malaria by the end of 2020. Surveillance and response are the most important components driving the whole process of the national malaria elimination programme (NMEP), under the technical guidance used in NMEP. This chapter introduces the evolution of the surveillance from the control to the elimination stages and the current structure of national surveillance system in China. When the NMEP launched, both routine surveillance and sentinel surveillance played critical role in monitoring the process of NMEP. In addition, the current response strategy of NMEP was also reviewed, including the generally developed "1-3-7 Strategy". More effective and sensitive risk assessment tools were introduced, which cannot only predict the trends of malaria, but also are important for the design and adjustment of the surveillance and response systems in the malaria elimination stage. Therefore, this review presents the landscape of malaria surveillance and response in China as well as their contribution to the NMEP, with a focus on activities for early detection of malaria cases, timely control of malaria foci and epidemics, and risk prediction. Furthermore, challenges and recommendations for accelerating NMEP through surveillance are put forward.
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Affiliation(s)
- Xin-Yu Feng
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Key Laboratory of Parasite and Vector Biology, MOH; WHO Collaborating Centre for Malaria, Schistosomiasis and Filariasis, Shanghai, People's Republic of China
| | - Zhi-Gui Xia
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Key Laboratory of Parasite and Vector Biology, MOH; WHO Collaborating Centre for Malaria, Schistosomiasis and Filariasis, Shanghai, People's Republic of China
| | - Sirenda Vong
- World Health Organization, China Representative Office, Beijing, People's Republic of China
| | - Wei-Zhong Yang
- Chinese Preventive Medicine Association, Beijing, People's Republic of China; Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China
| | - Shui-Sen Zhou
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Key Laboratory of Parasite and Vector Biology, MOH; WHO Collaborating Centre for Malaria, Schistosomiasis and Filariasis, Shanghai, People's Republic of China
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Zhao X, Chen F, Feng Z, Li X, Zhou XH. Characterizing the effect of temperature fluctuation on the incidence of malaria: an epidemiological study in south-west China using the varying coefficient distributed lag non-linear model. Malar J 2014; 13:192. [PMID: 24886630 PMCID: PMC4050477 DOI: 10.1186/1475-2875-13-192] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2014] [Accepted: 05/20/2014] [Indexed: 01/03/2023] Open
Abstract
Background Malaria transmission is strongly determined by the environmental temperature and the environment is rarely constant. Therefore, mosquitoes and parasites are not only exposed to the mean temperature, but also to daily temperature variation. Recently, both theoretical and laboratory work has shown, in addition to mean temperatures, daily fluctuations in temperature can affect essential mosquito and parasite traits that determine malaria transmission intensity. However, so far there is no epidemiological evidence at the population level to this problem. Methods Thirty counties in southwest China were selected, and corresponding weekly malaria cases and weekly meteorological variables were collected from 2004 to 2009. Particularly, maximum, mean and minimum temperatures were collected. The daily temperature fluctuation was measured by the diurnal temperature range (DTR), the difference between the maximum and minimum temperature. The distributed lag non-linear model (MDLNM) was used to study the correlation between weekly malaria incidences and weekly mean temperatures, and the correlation pattern was allowed to vary over different levels of daily temperature fluctuations. Results The overall non-linear patterns for mean temperatures are distinct across different levels of DTR. When under cooler temperature conditions, the larger mean temperature effect on malaria incidences is found in the groups of higher DTR, suggesting that large daily temperature fluctuations act to speed up the malaria incidence in cooler environmental conditions. In contrast, high daily fluctuations under warmer conditions will lead to slow down the mean temperature effect. Furthermore, in the group of highest DTR, 24-25°C or 21-23°C are detected as the optimal temperature for the malaria transmission. Conclusion The environment is rarely constant, and the result highlights the need to consider temperature fluctuations as well as mean temperatures, when trying to understand or predict malaria transmission. This work may be the first epidemiological study confirming that the effect of the mean temperature depends on temperature fluctuations, resulting in relevant evidence at the population level.
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Affiliation(s)
| | | | | | - Xiaosong Li
- West China School of Public Health, Sichuan University, No,17 Section 3, South Renmin Road, 610041 Chengdu, China.
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A mixed method to evaluate burden of malaria due to flooding and waterlogging in Mengcheng County, China: a case study. PLoS One 2014; 9:e97520. [PMID: 24830808 PMCID: PMC4022516 DOI: 10.1371/journal.pone.0097520] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2014] [Accepted: 04/20/2014] [Indexed: 11/19/2022] Open
Abstract
Background Malaria is a highly climate-sensitive vector-borne infectious disease that still represents a significant public health problem in Huaihe River Basin. However, little comprehensive information about the burden of malaria caused by flooding and waterlogging is available from this region. This study aims to quantitatively assess the impact of flooding and waterlogging on the burden of malaria in a county of Anhui Province, China. Methods A mixed method evaluation was conducted. A case-crossover study was firstly performed to evaluate the relationship between daily number of cases of malaria and flooding and waterlogging from May to October 2007 in Mengcheng County, China. Stratified Cox models were used to examine the lagged time and hazard ratios (HRs) of the risk of flooding and waterlogging on malaria. Years lived with disability (YLDs) of malaria attributable to flooding and waterlogging were then estimated based on the WHO framework of calculating potential impact fraction in the Global Burden of Disease study. Results A total of 3683 malaria were notified during the study period. The strongest effect was shown with a 25-day lag for flooding and a 7-day lag for waterlogging. Multivariable analysis showed that an increased risk of malaria was significantly associated with flooding alone [adjusted hazard ratio (AHR) = 1.467, 95% CI = 1.257, 1.713], waterlogging alone (AHR = 1.879, 95% CI = 1.696, 2.121), and flooding and waterlogging together (AHR = 2.926, 95% CI = 2.576, 3.325). YLDs per 1000 of malaria attributable to flooding alone, waterlogging alone and flooding and waterlogging together were 0.009 per day, 0.019 per day and 0.022 per day, respectively. Conclusion Flooding and waterlogging can lead to higher burden of malaria in the study area. Public health action should be taken to avoid and control a potential risk of malaria epidemics after these two weather disasters.
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