Recent studies have considered the connections between malaria incidence and climate variables using mathematical and statistical models. Some of the statistical models focused on time series approach based on Box–Jenkins methodology or on dynamic model. The latter approach allows for covariates different from its original lagged values, while the Box–Jenkins does not. In real situations, malaria incidence counts may turn up with many zero terms in the time series. Fitting time series model based on the Box–Jenkins approach and ARIMA may be spurious. In this study, a zero-inflated negative binomial regression model was formulated for fitting malaria incidence in Mopani and Vhembe―two of the epidemic district municipalities in Limpopo, South Africa. In particular, a zero-inflated negative binomial regression model was formulated for daily malaria counts as a function of some climate variables, with the aim of identifying the model that best predicts reported malaria cases. Results from this study show that daily rainfall amount and the average temperature at various lags have a significant influence on malaria incidence in the study areas. The significance of zero inflation on the malaria count was examined using the Vuong test and the result shows that zero-inflated negative binomial regression model fits the data better. A dynamical climate-based model was further used to investigate the population dynamics of mosquitoes over the two regions. Findings highlight the significant roles of Anopheles arabiensis on malaria transmission over the regions and suggest that vector control activities should be intense to eradicate malaria in Mopani and Vhembe districts. Although An. arabiensis has been identified as the major vector over these regions, our findings further suggest the presence of additional vectors transmitting malaria in the study regions. The findings from this study offer insight into climate-malaria incidence linkages over Limpopo province of South Africa.

Background: Sickle cell trait (SCT) refers to the carriage of one abnormal copy of the β-globin gene, the HbS allele. SCT offers protection against malaria, controlling parasite density and preventing progression to symptomatic malaria. However, it remains unclear whether SCT also affects transmission stages and mosquito infection parameters. Deciphering the impact of the SCT on human to mosquito malaria transmission is key to understanding mechanisms that maintain the trait in malaria endemic areas. Methods: The study was conducted from June to July 2017 among asymptomatic children living in the locality of Mfou, Cameroon. Blood samples were collected from asymptomatic children to perform malaria diagnosis by microscopy, Plasmodium species by PCR and hemoglobin typing by RFLP. Infectiousness of gametocytes to mosquitoes was assessed by membrane feeding assays using blood from gametocyte carriers of HbAA and HbAS genotypes. A zero-inflated model was fitted to predict distribution of oocysts in mosquitoes according to hemoglobin genotype of the gametocyte source. Results: Among the 1557 children enrolled in the study, 314 (20.16%) were of the HbAS genotype. The prevalence of children with P. falciparum gametocytes was 18.47% in HbAS individuals and 13.57% in HbAA, and the difference is significant (χ² = 4.61, P = 0.032). Multiplicity of infection was lower in HbAS gametocyte carriers (median = 2 genotypes/carrier in HbAS versus 3.5 genotypes/carrier in HbAA, Wilcoxon sum rank test = 188, P = 0.032). Gametocyte densities in the blood donor significantly influenced mosquito infection prevalence in both HbAS and HbAA individuals. The HbAS genotype had no significant effect on mosquito infection outcomes when using immune or naïve serum in feeding assays. In AB replacement feeding experiments, the odds ratio of mosquito infection for HbAA blood as compared to HbAS was 0.56 (95% CI 0.29–1.10), indicating a twice higher risk of infection in mosquitoes fed on gametocyte-containing blood of HbAS genotype. Conclusion: Plasmodium transmission stages were more prevalent in SCT individuals. This may reflect the parasite’s enhanced investment in the sexual stage to increase their survival rate when asexual replication is impeded. The public health impact of our results points the need for intensive malaria control interventions in areas with high prevalence of HbAS. The similar infection parameters in feeding experiments where mosquitoes received the original serum from the blood donor indicated that immune responses to gametocyte surface proteins occur in both HbAS and HbAA individuals. The higher risk of infection in mosquitoes fed on HbAS blood depleted of immune factors suggests that changes in the membrane properties in HbAS erythrocytes may impact on the maturation process of gametocytes within circulating red blood cells.

The recent resurgence of malaria incidence across epidemic regions in South Africa has been linked to climatic and environmental factors. An in-depth investigation of the impact of climate variability and mosquito abundance on malaria parasite incidence may therefore offer useful insight towards the control of this life-threatening disease. In this study, we investigate the influence of climatic factors on malaria transmission over Nkomazi Municipality. The variability and interconnectedness between the variables were analyzed using wavelet coherence analysis. Time-series analyses revealed that malaria cases significantly declined after the outbreak in early 2000, but with a slight increase from 2015. Furthermore, the wavelet coherence and time-lagged correlation analyses identified rainfall and abundance of Anopheles arabiensis as the major variables responsible for malaria transmission over the study region. The analysis further highlights a high malaria intensity with the variables from 1998–2002, 2004–2006, and 2010–2013 and a noticeable periodicity value of 256–512 days. Also, malaria transmission shows a time lag between one month and three months with respect to mosquito abundance and the different climatic variables. The findings from this study offer a better understanding of the importance of climatic factors on the transmission of malaria. The study further highlights the significant roles of An. arabiensis on malaria occurrence over Nkomazi. Implementing the mosquito model to predict mosquito abundance could provide more insight into malaria elimination or control in Africa.

The impact of climate variability on malaria transmission cannot be overemphasized. The interconnection between them could be well established using both statistical and deterministic models. However, this is difficult due to limited long-term malaria data over the study areas. In this article, analysis of simulated data from the previous study of Abiodun was carried out to draw important inference that offers in-depth understanding on climate-malaria incidence linkages over KwaZulu-Natal province. In particular, linear models based on stepwise regression were formulated for the number of exposed, infected and recovered individuals from malaria based on some climate variables and number of susceptible individuals to malaria. In fitting linear model to malaria data, care must be taken in ensuring that residuals of the model are not serially correlated. Ljung–Box test shows that residuals of the models are serially correlated. As a remedial measure, autoregressive integrated moving average model was fitted to the correlated residuals. In addition, it was found that an increase in daily rain amount and mean temperature significantly raises the chance of exposure to malaria while number of susceptible and exposed individuals affects transmission of malaria infection. The proportion of recovered individuals depends much on the number of malaria infected individuals rather than climatic variables.