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      Early warning climate indices for malaria and meningitis in tropical ecological zones

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          Abstract

          This study aims at assessing the impacts of climate indices on the spatiotemporal distribution of malaria and meningitis in Nigeria. The primary focus of the research is to develop an Early Warning System (EWS) for assessing climate variability implications on malaria and meningitis spread in the study area. Both climate and health data were used in the study to determine the relationship between climate variability and the occurrence of malaria and meningitis. The assessment was based on variations in different ecological zones in Nigeria. Two specific sample locations were randomly selected in each ecological zone for the analysis. The climatic data used in this study are dekadal precipitation, minimum and maximum temperature between 2000 and 2018, monthly aerosol optical depth between 2000 and 2018. The results show that temperature is relatively high throughout the year because the country is located in a tropical region. The significant findings of this study are that rainfall has much influence on the occurrence of malaria, while temperature and aerosol have more impact on meningitis. We found the degree of relationship between precipitation and malaria, there is a correlation coefficient R 2 ≥ 70.0 in Rainforest, Freshwater, and Mangrove ecological zones. The relationship between temperature and meningitis is accompanied by R 2 ≥ 72.0 in both Sahel and Sudan, while aerosol and meningitis harbour R 2 = 77.33 in the Sahel. The assessment of this initial data seems to support the finding that the occurrences of meningitis are higher in the northern region, especially the Sahel and Sudan. In contrast, malaria occurrence is higher in the southern part of the study area. In all, the multiple linear regression results revealed that rainfall was directly associated with malaria with β = 0.64, p = 0.001 but aerosol was directly associated with meningitis with β = 0.59, p < 0.001. The study concludes that variability in climatic elements such as low precipitation, high temperature, and aerosol may be the major drivers of meningitis occurrence.

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          Most cited references54

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          The global distribution of clinical episodes of Plasmodium falciparum malaria.

          Interest in mapping the global distribution of malaria is motivated by a need to define populations at risk for appropriate resource allocation and to provide a robust framework for evaluating its global economic impact. Comparison of older and more recent malaria maps shows how the disease has been geographically restricted, but it remains entrenched in poor areas of the world with climates suitable for transmission. Here we provide an empirical approach to estimating the number of clinical events caused by Plasmodium falciparum worldwide, by using a combination of epidemiological, geographical and demographic data. We estimate that there were 515 (range 300-660) million episodes of clinical P. falciparum malaria in 2002. These global estimates are up to 50% higher than those reported by the World Health Organization (WHO) and 200% higher for areas outside Africa, reflecting the WHO's reliance upon passive national reporting for these countries. Without an informed understanding of the cartography of malaria risk, the global extent of clinical disease caused by P. falciparum will continue to be underestimated.
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            Impact of climate change on global malaria distribution.

            Malaria is an important disease that has a global distribution and significant health burden. The spatial limits of its distribution and seasonal activity are sensitive to climate factors, as well as the local capacity to control the disease. Malaria is also one of the few health outcomes that has been modeled by more than one research group and can therefore facilitate the first model intercomparison for health impacts under a future with climate change. We used bias-corrected temperature and rainfall simulations from the Coupled Model Intercomparison Project Phase 5 climate models to compare the metrics of five statistical and dynamical malaria impact models for three future time periods (2030s, 2050s, and 2080s). We evaluated three malaria outcome metrics at global and regional levels: climate suitability, additional population at risk and additional person-months at risk across the model outputs. The malaria projections were based on five different global climate models, each run under four emission scenarios (Representative Concentration Pathways, RCPs) and a single population projection. We also investigated the modeling uncertainty associated with future projections of populations at risk for malaria owing to climate change. Our findings show an overall global net increase in climate suitability and a net increase in the population at risk, but with large uncertainties. The model outputs indicate a net increase in the annual person-months at risk when comparing from RCP2.6 to RCP8.5 from the 2050s to the 2080s. The malaria outcome metrics were highly sensitive to the choice of malaria impact model, especially over the epidemic fringes of the malaria distribution.
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              Malaria early warnings based on seasonal climate forecasts from multi-model ensembles.

              The control of epidemic malaria is a priority for the international health community and specific targets for the early detection and effective control of epidemics have been agreed. Interannual climate variability is an important determinant of epidemics in parts of Africa where climate drives both mosquito vector dynamics and parasite development rates. Hence, skilful seasonal climate forecasts may provide early warning of changes of risk in epidemic-prone regions. Here we discuss the development of a system to forecast probabilities of anomalously high and low malaria incidence with dynamically based, seasonal-timescale, multi-model ensemble predictions of climate, using leading global coupled ocean-atmosphere climate models developed in Europe. This forecast system is successfully applied to the prediction of malaria risk in Botswana, where links between malaria and climate variability are well established, adding up to four months lead time over malaria warnings issued with observed precipitation and having a comparably high level of probabilistic prediction skill. In years in which the forecast probability distribution is different from that of climatology, malaria decision-makers can use this information for improved resource allocation.
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                Author and article information

                Contributors
                aayanlade@oauife.edu.ng
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                31 August 2020
                31 August 2020
                2020
                : 10
                : 14303
                Affiliations
                [1 ]GRID grid.10824.3f, ISNI 0000 0001 2183 9444, Department of Geography, , Obafemi Awolowo University, ; Ile-Ife, Nigeria
                [2 ]GRID grid.17089.37, University of Alberta, ; Edmonton, Canada
                [3 ]GRID grid.10824.3f, ISNI 0000 0001 2183 9444, African Institute for Science Policy and Innovation, , Obafemi Awolowo University, ; Ile-Ife, Nigeria
                [4 ]GRID grid.10776.37, ISNI 0000 0004 1762 5517, PROMISE Department, , University of Palermo, ; Palermo, Italy
                Article
                71094
                10.1038/s41598-020-71094-8
                7459128
                32868821
                00382c42-9832-47d2-9af6-9bb6ef94be7c
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 January 2020
                : 10 August 2020
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                © The Author(s) 2020

                Uncategorized
                climate sciences,diseases
                Uncategorized
                climate sciences, diseases

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