Modelling of malaria incidence in Akure, Nigeria: negative binomial approach

Makinde, Olusola Samuel; Abiodun, Gbenga J.; Ojo, Oluwasegun

doi:10.1007/s10708-019-10134-x

Cited by 17 publications

(14 citation statements)

References 29 publications

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“… 44 The relative humidity was found to be negatively correlated to count of malaria incidence in this study. This finding is not parallel to the study finding of Akure, Nigeria, 45 Motuo county, Tibet, 40 and Baluchestan, Iran. 46 This might be due to the effect of high relative humidity of the area above which the survival of mosquito vector will be declining.…”

Section: Discussioncontrasting

confidence: 99%

Spatio-Temporal Variation of Malaria Incidence and Risk Factors in West Gojjam Zone, Northwest Ethiopia

Tegegne

Gelaye

Dessie

et al. 2022

Environ�Health�Insights

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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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Section: Discussioncontrasting

confidence: 99%

Spatio-Temporal Variation of Malaria Incidence and Risk Factors in West Gojjam Zone, Northwest Ethiopia

Tegegne

Gelaye

Dessie

et al. 2022

Environ�Health�Insights

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“…The potential impact of 1.5 °C and 2 °C global warming on consecutive dry and wet days has recently been investigated over the region (Klutse et al 2018). As shown in other studies in Senegal at local and national scales (Diouf et al 2013(Diouf et al , 2017, Benin (Ayanlade et al 2020), Nigeria (Makinde et al 2021), Tourre et al 2017and over West Africa (Diouf et al 2020. The risk of malaria transmission is primarily associated with climate parameters, particularly rainfall and temperature, with a 2-month lag observed between the rainfall peak in August and the peak in malaria cases in October.…”

Section: Introductionmentioning

confidence: 55%

“…In desert regions and highlands bordering malaria epidemic areas, transmission is unstable, and the human population is not immune (Pascual et al 2006;Menne and Bertollini 2000;Wandiga et al 2010;Alonso et al 2011). Climate variability and change have been linked with malaria transmission in most West African countries: Benin, Burkina Faso, Cabo Verde, Côte D'Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, and Togo (Ayanlade et al 2020, Diouf et al 2020Makinde et al 2021). For example, severe epidemics can occur when atmospheric conditions (precipitation and temperature) become favorable for transmission.…”

Section: Introductionmentioning

confidence: 99%

Impact of future climate change on malaria in West Africa

Diouf

Adeola

Abiodun

et al. 2021

Theor Appl Climatol

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Understanding the regional impact of future climate change is one of the major global challenges of this century. This study investigated possible effects of climate change on malaria in West Africa in the near future (2006–2035) and the far future (2036–2065) under two representative concentration pathway (RCP) scenarios (RCP4.5 and RCP8.5), compared to an observed evaluation period (1981–2010). Projected rainfall and temperature were obtained from the coordinated regional downscaling experiment (CORDEX) simulations of the Rossby Centre Regional Atmospheric regional climate model (RCA4). The malaria model used is the Liverpool malaria model (LMM), a dynamical malaria model driven by daily time series of rainfall and temperature obtained from the CORDEX data. Our results highlight the unimodal shape of the malaria prevalence distribution, and the seasonal malaria transmission contrast is closely linked to the latitudinal variation of the rainfall. Projections showed that the mean annual malaria prevalence would decrease in both climatological periods under both RCPs but with a larger magnitude of decreasing under the RCP8.5. We found that the mean malaria prevalence for the reference period is greater than the projected prevalence for 6 of the 8 downscaled GCMs. The study enhances understanding of how malaria is impacted under RCP4.5 and RCP8.5 emission scenarios. These results indicate that the southern area of West Africa is at most risk of epidemics, and the malaria control programs need extra effort and help to make the best use of available resources by stakeholders.

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“…Following some recent studies [ 19 ], a negative binomial regression model of the form log e ( E ( Y ) = β 0 + β 1 X 1 + β 2 X 2 + ... + β p X p + ɛ may be formulated for the total number of death cases based on incidence cases. However, it is assumed that incidence precedes mortality.…”

Section: Methodsmentioning

confidence: 99%

Comparison of Predictive Models and Impact Assessment of Lockdown for COVID-19 over the United States

Makinde¹,

Adeola²,

Abiodun³

et al. 2021

JEGH

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The novel Coronavirus Disease 2019 (COVID-19) recently gained attention as the virus continues to claim more lives globally. The disease hastily spread from Wuhan to further China provinces and other nations worldwide. Currently (as of August 10, 2020), more than 19.8 million cases have been confirmed with about 12.1 million recovered and 732,000 related deaths across the globe, as stated by the Johns Hopkins virus dashboard. At the beginning of the epidemic, elderly people were more susceptible to COVID-19 [1]. As the epidemic progressed, an increase in the number of cases among people between 45 and 64 years was recorded, as well as an upsurge in the number of cases among individuals, especially individuals between 18 and 44 years [2]. Reports also show that the cases are 2.6 times higher on Black/African American and 2.8 times higher on Hispanic/Latino individuals. Furthermore, COVID-19 induced death is nine times lower on 0-4 years old children and 630 times higher on 85+ years old adults [3]. The various signs associated with COVID-19 are fever, dry cough, short breath, and breathing difficulties. COVID-19 poses a severe threat to the health of individuals worldwide; on January 30, 2020, the World Health Organization declared a universal health emergence on COVID-19 [4,5]. On January 21, 2020, the COVID-19 index case was confirmed in the USA. Roughly a month after that (February 29, 2020), the first death was reported in Washington state. As of August 10, 2020, the USA has confirmed about 4.9 million cases and over 161,284 related deaths. At least 229,073 of those cases occurred in New York City, 184,

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Modelling of malaria incidence in Akure, Nigeria: negative binomial approach

Cited by 17 publications

References 29 publications

Spatio-Temporal Variation of Malaria Incidence and Risk Factors in West Gojjam Zone, Northwest Ethiopia

Spatio-Temporal Variation of Malaria Incidence and Risk Factors in West Gojjam Zone, Northwest Ethiopia

Impact of future climate change on malaria in West Africa

Comparison of Predictive Models and Impact Assessment of Lockdown for COVID-19 over the United States

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