Malaria still poses a significant threat in Nigeria despite the various efforts to abate its transmission. Certain environmental factors have been implicated to increase the risk of malaria in Nigeria and other affected countries. The study aimed to evaluate the spatial and temporal association between the incidence of malaria and some environmental risk factors in Nigeria. The study used malaria incidence and environmental risk factors data emanating from 2015 Nigeria Malaria Indicator Survey accessed from the Demographic and Health Survey database. A total of 333 and 326 clusters throughout the country were used for malaria incidence study and environmental variables respectively. The spatial autocorrelation of malaria incidence and hotspot analysis was determined by the Moran’s diagram and local Moran’s I index, respectively. The relationships between the malaria incidence and the ecological predictors of transmission were analysed in all the six geopolitical zones of Nigeria from 2000–2015 using ordinary least square (OLS), spatial lag model (SLM), and spatial error model (SEM). Annual rainfall, precipitation and proximity to water showed significant positive relationship with the incidence rate of malaria in the OLS model (P < 0.01), whereas aridity was negatively related to malaria incidence (P < 0.001) in the same model. The rate of incidence of malaria increased significantly with increase in temperature, aridity, rainfall and proximity to water in the SEM whereas only temperature and proximity to water have significant positive effect on malaria incidence in the SLM. The modelling of the ecological predictors of malaria transmission and spatial maps provided in this study could aid in developing framework to mitigate malaria and identify its hotspots for urgent intervention in the endemic regions.

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Modeling the relationship between malaria prevalence and insecticide-treated bed nets coverage using Leroux prior Bayesian spatial generalized linear mixed models ABSTRACT OBJECTIVES: To evaluate malaria transmission in relation to insecticide-treated nets (ITN) coverage in Nigeria. METHODS:We used exploratory analysis approach to evaluate the variation of malaria transmission and ITN distribution in 1325 demographic and health survey (DHS) clusters in Nigeria. A Bayesian Spatial Generalized Linear Mixed Model (BSGLMM) with Leroux conditional autoregressive prior for the random effects was used to model the spatial and contextual variation in malaria prevalence and ITN distribution after adjusting for environmental variables.RESULTS: Spatial smoothed maps showed a nationwide distribution of malaria and ITN. The distribution of ITN varied significantly across the six geopolitical zones (P<0.05). The North-East had the least ITN distribution (0.196±0.071) while ITN distribution was highest in the South-South (0.309±0.075). ITN coverage was also higher in the rural (0.281±0.074) than in the urban areas (0.240±0.096) (P<0.05). The Bayesian hierarchical regression results showed a nonsignificant negative relationship between malaria prevalence and ITN coverage but a significant spatial structured random effect and unstructured random effect. The correlates of malaria transmission include; rainfall, maximum temperature and proximity to water. CONCLUSIONS:Reduction in malaria transmission was not significantly related to ITN coverage though much could be achieved in an attempt to curtail malaria transmission with an enhanced ITN coverage. A multi and integrated approach to malaria control is strongly advocated.

Background Malaria has been strongly linked to the transmission and pathophysiology of some viral diseases. Malaria and vaccine-preventable diseases often co-exist in endemic countries but the implication of their co-existence on their transmission dynamics and control is poorly understood. The study aims to evaluate the relationships between the incidence of malaria and cases of measles and yellow fever in Africa. Methods The malaria incidence, death due to malaria, measles and yellow fever data were sourced from the WHO database. Poisson and zero-inflated time-trend regression were used to model the relationships between malaria and the two vaccine-preventable diseases. P-values <0.05 were considered statistically significant. Results A significant negative relationship existed between malaria incidence and measles cases (P<0.05), however, malaria showed a positive relationship with yellow fever (P<0.05). The relationships between death due to malaria and measles/yellow fever cases followed similar trends but with a higher level of statistical significance (P<0.001). Conclusions Malaria varied negatively with measles cases but positively with yellow fever. The relationships observed in this study could be important for the management of malaria and the studied vaccine-preventable diseases. Increase vaccination coverage and/or malaria treatment could modulate the direction of these relationships.

Aims: One of the ways to manage the current coronavirus disease 2019 (COVID-19) pandemic is monitoring of public knowledge, risk perceptions, adherence to preventive measures and preparedness behaviors. This is of utmost importance in resource limited countries. This study determined the knowledge and perception about COVID-19; adherence to COVID-19 preventive measures; as well as predictors of self-perceived risk of contracting COVID-19 among Nigerian adults.Methods: A cross-sectional study was conducted among Nigerian adults ≥ 18 years using an online survey. Participants were recruited using the authors’ social networks. Data was analysed using descriptive and inferential statistics at 5% level of statistical significance.Results: Generally, a high proportion of respondents had correct knowledge about COVID-19. However, only about half (49.8% and 49.9%) had correct knowledge that obesity was a risk factor for COVID-19 and that antibiotics cannot be used to treat COVID-19. Most (84.1%) did not have a self-perceived risk of contracting COVID-19. Most (81.0%) have been avoiding crowded places and 61.3% washed their hands very often. Predictors of self-perceived risk of COVID-19 were age 40-59 years (OR 2.05, CI 1.217-3.435), ≥ 60 years (OR 4.68, CI 1.888-11.583) and visiting crowded places (OR 2.27, CI 1.499-3.448).Conclusion: Our study recommends more rigorous public health education aimed at improving COVID-19 outbreak response in Nigerian. Also, physical and social distancing should be emphasized across all age groups with additional focus on the older population.