Cholera is one of the most important climate sensitive diseases in Nigeria that pose a threat to public health because of its fatality and endemic nature. This study aims to investigate the influences of meteorological and socioeconomic factors on the spatiotemporal variability of cholera morbidity and mortality in Nigeria. Stepwise multiple regression and generalised additive models were fitted for individual states as well as for three groups of the states based on annual precipitation. Different meteorological variables were analysed, taking into account socioeconomic factors that are potentially enhancing vulnerability (e.g. absolute poverty, adult literacy, access to pipe borne water). Results quantify the influence of both climate and socioeconomic variables in explaining the spatial and temporal variability of the disease incidence and mortality. Regional importance of different factors is revealed, which will allow further insight into the disease dynamics. Additionally, cross validated models suggest a strong possibility of disease prediction, which will help authorities to put effective control measures in place which depend on prevention, and or efficient response.

Northwest Nigeria is a region with a high risk of meningitis. In this study, the influence of climate on monthly meningitis incidence was examined. Monthly counts of clinically diagnosed hospital-reported cases of meningitis were collected from three hospitals in northwest Nigeria for the 22-yr period spanning 1990-2011. Generalized additive models and generalized linear models were fitted to aggregated monthly meningitis counts. Explanatory variables included monthly time series of maximum and minimum temperature, humidity, rainfall, wind speed, sunshine, and dustiness from weather stations nearest to the hospitals, and the number of cases in the previous month. The effects of other unobserved seasonally varying climatic and nonclimatic risk factors that may be related to the disease were collectively accounted for as a flexible monthly varying smooth function of time in the generalized additive models, s(t). Results reveal that the most important explanatory climatic variables are the monthly means of daily maximum temperature, relative humidity, and sunshine with no lag; and dustiness with a 1-month lag. Accounting for s(t) in the generalized additive models explains more of the monthly variability of meningitis compared to those generalized linear models that do not account for the unobserved factors that s(t) represents. The skill score statistics of a model version with all explanatory variables lagged by 1 month suggest the potential to predict meningitis cases in northwest Nigeria up to a month in advance to aid decision makers.

Meningitis remains a major health burden throughout Sahelian Africa, especially in heavily populated northwest Nigeria with an annual incidence rate ranging from 18 to 200 per 100 000 people for 2000–11. Several studies have established that cases exhibit sensitivity to intra- and interannual climate variability, peaking during the hot and dry boreal spring months, raising concern that future climate change may increase the incidence of meningitis in the region. The impact of future climate change on meningitis risk in northwest Nigeria is assessed by forcing an empirical model of meningitis with monthly simulations of seven meteorological variables from an ensemble of 13 statistically downscaled global climate model projections from phase 5 of the Coupled Model Intercomparison Experiment (CMIP5) for representative concentration pathway (RCP) 2.6, 6.0, and 8.5 scenarios, with the numbers representing the globally averaged top-of-the-atmosphere radiative imbalance (in W m−2) in 2100. The results suggest future temperature increases due to climate change have the potential to significantly increase meningitis cases in both the early (2020–35) and late (2060–75) twenty-first century, and for the seasonal onset of meningitis to begin about a month earlier on average by late century, in October rather than November. Annual incidence may increase by 47% ± 8%, 64% ± 9%, and 99% ± 12% for the RCP 2.6, 6.0, and 8.5 scenarios, respectively, in 2060–75 with respect to 1990–2005. It is noteworthy that these results represent the climatological potential for increased cases due to climate change, as it is assumed that current prevention and treatment strategies will remain similar in the future.

Cholera is one of the infectious diseases that remains a major health burden in WestAfrica and especially in Nigeria. Several studies have raised concern that climate change may exacerbate the risk of the disease in the future. Projecting the future risk of this disease is essential, especially for regions where the projected climate change impacts, and infectious disease risk, are both large. Projections were made by forcing an empirical model of cholera with monthly simulations of four meteorological variables from an ensemble of ten statistically downscaled global climate model projections for Representative Concentration Pathways 2.6, 6.0 and 8.5 scenarios. Result indicates statistically significant increases in cases during April-September for RCPs 6.0 and 8.5 in both near (2020-2035) and far (2060-2075) future. The months with the largest increases coincide with the months (May and June) in which maximum temperature increases are also large. Cases only showed potential increases in the wettest months of July and August in the far future projections for RCPs 6.5 (8.3 and 7.9%) and 8.0 (17 and 21%) respectively.

This study aimed at characterising the spatiotemporal pattern of meningitis in Nigeria, in order to detect vulnerable areas. Stratified sampling was deployed in research inquiry based on the following variables: geographic location; population density; poverty status; and adult literacy level in accordance to diseases incidence rate. Population-based ratios were then computed for each stratum for the identification of high risk areas. Global Moran's Index spatial autocorrelation technique was additionally used to investigate the extent to which neighbouring values of incidence rate are correlated and to determine meningitis demographic risk factors. A significant spatial clustering of meningitis incidence rate has been found in northern Nigeria, with less or no clustering in the southern part of the country. A significant correlation of disease, with geographical location and poverty was perceived. The study commends that improving the socioeconomic status of the affected population may reduce the incidents of meningitis in the high risk area in Nigeria.