Background. South Africa (SA) in general, and KwaZulu-Natal (KZN) Province in particular, have stepped up efforts to eliminate malaria. To strengthen malaria control in KZN, a relevant malaria forecasting model is important. Objectives. To develop a forecasting model to predict malaria cases in KZN using the Seasonal Autoregressive Integrated Moving Average (SARIMA) time series approach. Methods. The study was carried out retrospectively using a clinically confirmed monthly malaria case dataset that was split into two. The first dataset (January 2005 -December 2013) was used to construct a SARIMA model by adopting the Box-Jenkins approach, while the second dataset (January -December 2014) was used to validate the forecast generated from the best-fit model. Results. Three plausible models were identified, and the SARIMA (0,1,1)(0,1,1) 12 model was selected as the best-fit model. This model was used to forecast malaria cases during 2014, and it was observed to fit closely with malaria cases reported in 2014. Conclusions. The SARIMA (0,1,1)(0,1,1) 12 model could serve as a useful tool for modelling and forecasting monthly malaria cases in KZN. It could therefore play a key role in shaping malaria control and elimination efforts in the province.

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Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of KnowledgeSM databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably.

The change of the malaria control intervention policy in South Africa (SA), re-introduction of dichlorodiphenyltrichloroethane (DDT), may be responsible for the low and sustained malaria transmission in KwaZulu-Natal (KZN). We evaluated the effect of the re-introduction of DDT on malaria in KZN and suggested practical ways the province can strengthen her already existing malaria control and elimination efforts, to achieve zero malaria transmission. We obtained confirmed monthly malaria cases in KZN from the malaria control program of KZN from 1998 to 2014. The seasonal autoregressive integrated moving average (SARIMA) intervention time series analysis (ITSA) was employed to model the effect of the re-introduction of DDT on confirmed monthly malaria cases. The result is an abrupt and permanent decline of monthly malaria cases (w=-1174.781, p-value=0.003) following the implementation of the intervention policy. The sustained low malaria cases observed over a long period suggests that the continued usage of DDT did not result in insecticide resistance as earlier anticipated. It may be due to exophagic malaria vectors, which renders the indoor residual spraying not totally effective. Therefore, the feasibility of reducing malaria transmission to zero in KZN requires other reliable and complementary intervention resources to optimize the existing ones.

The global spread of the coronavirus infections disease − 2019 (COVID-19) and the search for new drugs from natural products particularly from plants are receiving much attention recently. In this study, the therapeutic potential of a new iridoid glycoside isolated from the leaves of Clerodendrum volubile against COVID-19 was investigated. Harpagide 5-O-β-D-glucopyranoside (HG) was isolated, characterised and investigated for its druglikeness, optimized geometry, and pharmacokinetics properties. Its immunomodulatory was determined by chemiluminescence assay using polymorphonuclear neutrophils (PMNs) in addition to T-cell proliferation assay. In silico analysis was used in determining its molecular interaction with severe acute respiratory syndrome coronavirus-2 (SARS-COV-2). HG displayed potent druglikeness properties, with no inhibitory effect on cytochrome P 450 (1A2, 2C19, 2C9, 2D6 and 3A4) and a predicted LD 50 of 2000 mg/kg. Its 1 H-NMR chemical shifts showed a little deviation of 0.01 and 0.11 ppm for H-4 and H-9, respectively. HG significantly suppressed oxidative bursts in PMNs, while concomitantly inhibiting T-cell proliferation. It also displayed a very strong binding affinity with the translation initiation and termination sequence sites of spike (S) protein mRNA of SARS-COV-2, its gene product, and host ACE2 receptor. These results suggest the immunomodulatory properties and anti-SARS-COV-2 potentials of HG which can be explored in the treatment and management of COVID-19. Communicated by Ramaswamy H. Sarma

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Background: Understanding the socioeconomic status that influences malaria transmission in KwaZulu-Natal, South Africa is vital in creating policies and strategies to combat malaria transmission, improve socioeconomic conditions and strengthen the malaria elimination campaign. Objectives: To determine the relationship between socioeconomic status and malaria incidence in KwaZulu-Natal, South Africa. Methods: Socioeconomic information (gender, age, no formal education, no electricity, no toilet facilities, unemployment) and malaria data for 2011 were obtained from Statistics South Africa and the malaria control program of KwaZulu-Natal, South Africa respectively. The analysis was conducted employing the Bayesian multiple regression model. Results: The obtained posterior samples show that all the variables employed in this study were significant and positive predictors of malaria disease at 95% credible interval. The low socioeconomic status that exhibited the strongest association with malaria risk was lack of toilet facilities (odd ratio =12.39; 95% credible interval = 0.61, 24.36). This was followed by no formal education (odd ratio =11.11; 95% credible interval = 0.51, 24.10) and lack of electricity supply (odd ratio =8.94; 95% credible interval = 0.31, 23.21) respectively. Conclusions: Low socioeconomic status potentially sustains malaria transmission and burden. As an implication, poverty alleviation and malaria intervention resources should be incorporated side by side into the socioeconomic framework to attain zero malaria transmission. Keywords: Malaria; socialeconomic status; Bayesian modelling; KwaZulu-Natal; South Africa.