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Generalized Linear Models to Forecast Malaria Incidence in Three Endemic Regions of Senegal
Affecting millions of individuals yearly, malaria is one of the most dangerous and deadly tropical diseases. It is a major global public health problem, with an alarming spread of parasite transmitted by mosquito (Anophele). Various studies have emerged that construct a mathematical and statistical...
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| Published in: | International journal of environmental research and public health 2023-07, Vol.20 (13), p.6303 |
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| container_issue | 13 |
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| container_title | International journal of environmental research and public health |
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| creator | Diao, Ousmane Absil, P-A Diallo, Mouhamadou |
| description | Affecting millions of individuals yearly, malaria is one of the most dangerous and deadly tropical diseases. It is a major global public health problem, with an alarming spread of parasite transmitted by mosquito (Anophele). Various studies have emerged that construct a mathematical and statistical model for malaria incidence forecasting. In this study, we formulate a generalized linear model based on Poisson and negative binomial regression models for forecasting malaria incidence, taking into account climatic variables (such as the monthly rainfall, average temperature, relative humidity), other predictor variables (the insecticide-treated bed-nets (ITNs) distribution and Artemisinin-based combination therapy (ACT)) and the history of malaria incidence in Dakar, Fatick and Kedougou, three different endemic regions of Senegal. A forecasting algorithm is developed by taking the meteorological explanatory variable Xj at time t-lj, where
is the observation time and lj is the lag in Xj that maximizes its correlation with the malaria incidence. We saturated the rainfall in order to reduce over-forecasting. The results of this study show that the Poisson regression model is more adequate than the negative binomial regression model to forecast accurately the malaria incidence taking into account some explanatory variables. The application of the saturation where the over-forecasting was observed noticeably increases the quality of the forecasts. |
| doi_str_mv | 10.3390/ijerph20136303 |
| format | article |
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is the observation time and lj is the lag in Xj that maximizes its correlation with the malaria incidence. We saturated the rainfall in order to reduce over-forecasting. The results of this study show that the Poisson regression model is more adequate than the negative binomial regression model to forecast accurately the malaria incidence taking into account some explanatory variables. The application of the saturation where the over-forecasting was observed noticeably increases the quality of the forecasts.</description><identifier>ISSN: 1660-4601</identifier><identifier>ISSN: 1661-7827</identifier><identifier>EISSN: 1660-4601</identifier><identifier>DOI: 10.3390/ijerph20136303</identifier><identifier>PMID: 37444150</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Artemisinin ; Binomial distribution ; Business metrics ; Climate ; Climate change ; Forecasting ; Generalized linear models ; Humans ; Humidity ; Incidence ; Insecticides ; Linear Models ; Machine learning ; Malaria ; Malaria - epidemiology ; Mathematical models ; Methods ; Models, Statistical ; Parameter estimation ; Parasites ; Poisson density functions ; Public health ; Rain ; Rainfall ; Regression analysis ; Relative humidity ; Senegal - epidemiology ; Statistical analysis ; Statistical models ; Temperature ; Variables ; Vector-borne diseases</subject><ispartof>International journal of environmental research and public health, 2023-07, Vol.20 (13), p.6303</ispartof><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 by the authors. 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3343-ec64b64af5d72c36b1db772ad617f547ba80adffe4d9ae9da7fc2053c7d850753</citedby><cites>FETCH-LOGICAL-c3343-ec64b64af5d72c36b1db772ad617f547ba80adffe4d9ae9da7fc2053c7d850753</cites><orcidid>0000-0003-3598-9835</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2836351893/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2836351893?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,315,733,786,790,891,25783,27957,27958,37047,37048,38551,43930,44625,53827,53829,74769,75483</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37444150$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Diao, Ousmane</creatorcontrib><creatorcontrib>Absil, P-A</creatorcontrib><creatorcontrib>Diallo, Mouhamadou</creatorcontrib><title>Generalized Linear Models to Forecast Malaria Incidence in Three Endemic Regions of Senegal</title><title>International journal of environmental research and public health</title><addtitle>Int J Environ Res Public Health</addtitle><description>Affecting millions of individuals yearly, malaria is one of the most dangerous and deadly tropical diseases. It is a major global public health problem, with an alarming spread of parasite transmitted by mosquito (Anophele). Various studies have emerged that construct a mathematical and statistical model for malaria incidence forecasting. In this study, we formulate a generalized linear model based on Poisson and negative binomial regression models for forecasting malaria incidence, taking into account climatic variables (such as the monthly rainfall, average temperature, relative humidity), other predictor variables (the insecticide-treated bed-nets (ITNs) distribution and Artemisinin-based combination therapy (ACT)) and the history of malaria incidence in Dakar, Fatick and Kedougou, three different endemic regions of Senegal. A forecasting algorithm is developed by taking the meteorological explanatory variable Xj at time t-lj, where
is the observation time and lj is the lag in Xj that maximizes its correlation with the malaria incidence. We saturated the rainfall in order to reduce over-forecasting. The results of this study show that the Poisson regression model is more adequate than the negative binomial regression model to forecast accurately the malaria incidence taking into account some explanatory variables. The application of the saturation where the over-forecasting was observed noticeably increases the quality of the forecasts.</description><subject>Artemisinin</subject><subject>Binomial distribution</subject><subject>Business metrics</subject><subject>Climate</subject><subject>Climate change</subject><subject>Forecasting</subject><subject>Generalized linear models</subject><subject>Humans</subject><subject>Humidity</subject><subject>Incidence</subject><subject>Insecticides</subject><subject>Linear Models</subject><subject>Machine learning</subject><subject>Malaria</subject><subject>Malaria - epidemiology</subject><subject>Mathematical models</subject><subject>Methods</subject><subject>Models, Statistical</subject><subject>Parameter estimation</subject><subject>Parasites</subject><subject>Poisson density functions</subject><subject>Public health</subject><subject>Rain</subject><subject>Rainfall</subject><subject>Regression analysis</subject><subject>Relative humidity</subject><subject>Senegal - epidemiology</subject><subject>Statistical analysis</subject><subject>Statistical models</subject><subject>Temperature</subject><subject>Variables</subject><subject>Vector-borne diseases</subject><issn>1660-4601</issn><issn>1661-7827</issn><issn>1660-4601</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>COVID</sourceid><sourceid>PIMPY</sourceid><recordid>eNpdkc1PGzEQxS1UBDTlyrGy1EsvAXvHa--eqgoBRQpConDiYM3as4mjjZ3aCVL713cRHwJOM9L85mnePMaOpDgGaMVJWFJeLyohQYOAHXYgtRZTpYX89KbfZ59LWQoBjdLtHtsHo5SStThg9xcUKeMQ_pHnsxAJM79KnobCN4mfp0wOy4Zf4YA5IL-MLniKjniI_HaRifhZ9LQKjt_QPKRYeOr571FzjsMXttvjUOjwuU7Y3fnZ7emv6ez64vL052zqABRMyWnVaYV97U3lQHfSd8ZU6LU0fa1Mh41A3_ekfIvUejS9q0QNzvimFqaGCfvxpLvedivyjuJmdGTXOaww_7UJg30_iWFh5-nBSgFKqvFvE_b9WSGnP1sqG7sKxdEwYKS0LbZqoKmUMlU1ot8-oMu0zXH090hpqGXTwkgdP1Eup1Iy9a_XSGEfg7PvgxsXvr718Iq_JAX_AQqolWk</recordid><startdate>20230705</startdate><enddate>20230705</enddate><creator>Diao, Ousmane</creator><creator>Absil, P-A</creator><creator>Diallo, Mouhamadou</creator><general>MDPI AG</general><general>MDPI</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-3598-9835</orcidid></search><sort><creationdate>20230705</creationdate><title>Generalized Linear Models to Forecast Malaria Incidence in Three Endemic Regions of Senegal</title><author>Diao, Ousmane ; Absil, P-A ; Diallo, Mouhamadou</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3343-ec64b64af5d72c36b1db772ad617f547ba80adffe4d9ae9da7fc2053c7d850753</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Artemisinin</topic><topic>Binomial distribution</topic><topic>Business metrics</topic><topic>Climate</topic><topic>Climate change</topic><topic>Forecasting</topic><topic>Generalized linear models</topic><topic>Humans</topic><topic>Humidity</topic><topic>Incidence</topic><topic>Insecticides</topic><topic>Linear Models</topic><topic>Machine learning</topic><topic>Malaria</topic><topic>Malaria - epidemiology</topic><topic>Mathematical models</topic><topic>Methods</topic><topic>Models, Statistical</topic><topic>Parameter estimation</topic><topic>Parasites</topic><topic>Poisson density functions</topic><topic>Public health</topic><topic>Rain</topic><topic>Rainfall</topic><topic>Regression analysis</topic><topic>Relative humidity</topic><topic>Senegal - epidemiology</topic><topic>Statistical analysis</topic><topic>Statistical models</topic><topic>Temperature</topic><topic>Variables</topic><topic>Vector-borne diseases</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Diao, Ousmane</creatorcontrib><creatorcontrib>Absil, P-A</creatorcontrib><creatorcontrib>Diallo, Mouhamadou</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>International journal of environmental research and public health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Diao, Ousmane</au><au>Absil, P-A</au><au>Diallo, Mouhamadou</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Generalized Linear Models to Forecast Malaria Incidence in Three Endemic Regions of Senegal</atitle><jtitle>International journal of environmental research and public health</jtitle><addtitle>Int J Environ Res Public Health</addtitle><date>2023-07-05</date><risdate>2023</risdate><volume>20</volume><issue>13</issue><spage>6303</spage><pages>6303-</pages><issn>1660-4601</issn><issn>1661-7827</issn><eissn>1660-4601</eissn><notes>ObjectType-Article-1</notes><notes>SourceType-Scholarly Journals-1</notes><notes>ObjectType-Feature-2</notes><notes>content type line 23</notes><notes>These authors contributed equally to this work.</notes><notes>The first author is supported by a fellowship awarded by UCLouvain’s Conseil de l’action internationale.</notes><abstract>Affecting millions of individuals yearly, malaria is one of the most dangerous and deadly tropical diseases. It is a major global public health problem, with an alarming spread of parasite transmitted by mosquito (Anophele). Various studies have emerged that construct a mathematical and statistical model for malaria incidence forecasting. In this study, we formulate a generalized linear model based on Poisson and negative binomial regression models for forecasting malaria incidence, taking into account climatic variables (such as the monthly rainfall, average temperature, relative humidity), other predictor variables (the insecticide-treated bed-nets (ITNs) distribution and Artemisinin-based combination therapy (ACT)) and the history of malaria incidence in Dakar, Fatick and Kedougou, three different endemic regions of Senegal. A forecasting algorithm is developed by taking the meteorological explanatory variable Xj at time t-lj, where
is the observation time and lj is the lag in Xj that maximizes its correlation with the malaria incidence. We saturated the rainfall in order to reduce over-forecasting. The results of this study show that the Poisson regression model is more adequate than the negative binomial regression model to forecast accurately the malaria incidence taking into account some explanatory variables. The application of the saturation where the over-forecasting was observed noticeably increases the quality of the forecasts.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>37444150</pmid><doi>10.3390/ijerph20136303</doi><orcidid>https://orcid.org/0000-0003-3598-9835</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Artemisinin Binomial distribution Business metrics Climate Climate change Forecasting Generalized linear models Humans Humidity Incidence Insecticides Linear Models Machine learning Malaria Malaria - epidemiology Mathematical models Methods Models, Statistical Parameter estimation Parasites Poisson density functions Public health Rain Rainfall Regression analysis Relative humidity Senegal - epidemiology Statistical analysis Statistical models Temperature Variables Vector-borne diseases |
| title | Generalized Linear Models to Forecast Malaria Incidence in Three Endemic Regions of Senegal |
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