International Journal of Ecological Economics and Statistics, 2018
Cancer and hepatitis are global infectious diseases claiming millions of lives every year. In thi... more Cancer and hepatitis are global infectious diseases claiming millions of lives every year. In this study, we develop and analyse a cancer and hepatitis co-infection model to examine the relationship between the two diseases. In addition, we derive and analyse the reproduction numbers of each and both diseases. We explore the sensitivity of the reproduction numbers of both diseases to parameters, and performed the numerical simulations of the model. Our analyses show that the cancer-hepatitis model has disease-free equilibrium whenever the reproduction number is less than unity, and has a unique endemic equilibrium if the reproduction number is greater than one. Also our analyses highlight the impact of hepatitis on cancer and vise versa. Furthermore, our numerical solutions indicate that the carrying capacity and progression rate from susceptible to hepatitis and cancer play crucial roles in determining the population dynamics of both diseases. Although, our results highlight the im...
International Journal of Ecological Economics and Statistics, 2017
In this study, a comprehensive deterministic model for the transmission of malaria is develop and... more In this study, a comprehensive deterministic model for the transmission of malaria is develop and analyse. We consider in detailed the human population and both stages (aquatic and adult) of mosquito population are also included. We analyse the basic reproduction number, R0, and examine the existence and stability of equilibria. In an attempt to examine the effectiveness of control measure, we compute the sensitivity analysis of the reproductive number and highlight the importance of mosquito biting rate on malaria transmission. Finally, we perform the numerical simulations to verify the global stability of both diseasefree and endemic equilibrium.
A mathematical model for studying the impact of climate variability on malaria epidemics in South... more A mathematical model for studying the impact of climate variability on malaria epidemics in South Africa Gbenga Jacob Abiodun PhD thesis, Department of Mathematics and Applied Mathematics, Faculty of Natural Sciences, University of the Western Cape Malaria is most prevalent in tropical climates, where there are sufficient rainfall for mosquitoes to breed and conducive temperatures for both the mosquito and protozoa to live. A slight change in temperature can drastically affect the lifespan and patterns of mosquitoes, and moreover, the protozoan itself can only survive in a certain temperature range. With higher temperatures, mosquitoes can mature faster, and thus have more time to spread the disease. The malaria parasite also matures more quickly at warmer temperatures. However, if temperatures become too high, neither mosquitoes nor the malaria pathogen can survive. In addition, stagnant water is also a major contributor to the spread of malaria, since most mosquito species breed i...
In this study we revisit a theme of F. Nyabadza et al. [Journal of Biological Systems 18 No.2 (20... more In this study we revisit a theme of F. Nyabadza et al. [Journal of Biological Systems 18 No.2 (2010), 357–375], studying the optimal control of rolling out a public health education campaign and providing some further insights. We produce a Lyapunov function to prove global stability of the disease free equilibrium, and include a sensitivity analysis of the parameters and state variables. Finally, we present an optimal control problem on the implementation strategy of public health education, together with its solution. Various simulations are provided to illustrate the control problem.
The novel Coronavirus Disease 2019 (COVID-19) remains a worldwide threat to community health, soc... more The novel Coronavirus Disease 2019 (COVID-19) remains a worldwide threat to community health, social stability, and economic development. Since the first case was recorded on December 29, 2019, in Wuhan of China, the disease has rapidly extended to other nations of the world to claim many lives, especially in the USA, the United Kingdom, and Western Europe. To stay ahead of the curve consequent of the continued increase in case and mortality, predictive tools are needed to guide adequate response. Therefore, this study aims to determine the best predictive models and investigate the impact of lockdown policy on the USA’ COVID-19 incidence and mortality. This study focuses on the statistical modelling of the USA daily COVID-19 incidence and mortality cases based on some intuitive properties of the data such as overdispersion and autoregressive conditional heteroscedasticity. The impact of the lockdown policy on cases and mortality was assessed by comparing the USA incidence case with that of Sweden where there is no strict lockdown. Stochastic models based on negative binomial autoregressive conditional heteroscedasticity [NB INGARCH (p,q)], the negative binomial regression, the autoregressive integrated moving average model with exogenous variables (ARIMAX) and without exogenous variables (ARIMA) models of several orders are presented, to identify the best fitting model for the USA daily incidence cases. The performance of the optimal NB INGARCH model on daily incidence cases was compared with the optimal ARIMA model in terms of their Akaike Information Criteria (AIC). Also, the NB model, ARIMA model and without exogenous variables are formulated for USA daily COVID-19 death cases. It was observed that the incidence and mortality cases show statistically significant increasing trends over the study period. The USA daily COVID-19 incidence is autocorrelated, linear and contains a structural break but exhibits autoregressive conditional heteroscedasticity. Observed data are compared with the fitted data from the optimal models. The results further indicate that the NB INGARCH fits the observed incidence better than ARIMA while the NB models perform better than the optimal ARIMA and ARIMAX models for death counts in terms of AIC and root mean square error (RMSE). The results show a statistically significant relationship between the lockdown policy in the USA and incidence and death counts. This suggests the efficacy of the lockdown policy in the USA.
Understanding the regional impact of future climate change is one of the major global challenges ... more 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 an...
We present a compartmental model in ordinary differential equations of malaria disease transmissi... more We present a compartmental model in ordinary differential equations of malaria disease transmission, accommodating the effect of indoor residual spraying on the vector population. The model allows for influx of infected migrants into the host population and for outflow of recovered migrants. The system is shown to have positive solutions. In the special case of no infected immigrants, we prove global stability of the disease-free equilibrium. Existence of a unique endemic equilibrium point is also established for the case of positive influx of infected migrants. As a case study we consider the combined South African malaria region. Using data covering 31 years, we quantify the effect of malaria infected immigrants on the South African malaria region.
We present and analyze a stochastic differential equation (SDE) model for the population dynamics... more We present and analyze a stochastic differential equation (SDE) model for the population dynamics of a mosquito-borne infectious disease. We prove the solutions to be almost surely positive and global. We introduce a numerical invariant ${\mathcal{R}}$ R of the model with ${\mathcal{R}}<1$ R < 1 being a condition guaranteeing the almost sure stability of the disease-free equilibrium. We show that stochastic perturbations enhance the stability of the disease-free equilibrium of the underlying deterministic model. We illustrate the main stability theorem through simulations and show how to obtain interval estimates when making forward projections. We consulted a wide range of literature to find relevant numerical parameter values.
The connections between malaria incidence and climate variability have been studied in recent tim... more The connections between malaria incidence and climate variability have been studied in recent time using some mathematical and statistical models. Many of the statistical models in literature focused on time series approach based on Box–Jenkins methodology. However, fitting time series model based on the Box–Jenkins methodology may be challenging. Most malaria incidence data are count and are over-dispersed. In this study, negative binomial models were formulated for fitting malaria incidence in Akure—one of the epidemic cities in Nigeria. In particular, negative binomial models were formulated for each of the number of outpatient individuals, number of inpatient individuals and mortality count as a function of some climate variables. It was found that an increase in minimum temperature and relative humidity at lag 1 significantly increased the chance of malaria transmission and thereby leads to an increase in the number of inpatient and outpatient individuals, as well as the total number of malaria cases. The minimum temperature, rainfall amount and relative humidity of the study area have a significant impact on the increase of number of inpatient and outpatient individuals while mortality count depends on the total number of reported malaria cases. The findings from this study is to offer in-depth understanding on climate-malaria incidence linkages in Akure, Nigeria.
International Journal of Environmental Research and Public Health
This contribution aims to investigate the influence of monthly total rainfall variations on malar... more This contribution aims to investigate the influence of monthly total rainfall variations on malaria transmission in the Limpopo Province. For this purpose, monthly total rainfall was interpolated from daily rainfall data from weather stations. Annual and seasonal trends, as well as cross-correlation analyses, were performed on time series of monthly total rainfall and monthly malaria cases in five districts of Limpopo Province for the period of 1998 to 2017. The time series analysis indicated that an average of 629.5 mm of rainfall was received over the period of study. The rainfall has an annual variation of about 0.46%. Rainfall amount varied within the five districts, with the northeastern part receiving more rainfall. Spearman’s correlation analysis indicated that the total monthly rainfall with one to two months lagged effect is significant in malaria transmission across all the districts. The strongest correlation was noticed in Vhembe (r = 0.54; p-value = <0.001), Mopani (...
Communications in Statistics: Case Studies, Data Analysis and Applications
Abstract The impact of climate variability on malaria transmission cannot be overemphasized. The ... more Abstract 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.
Background Malaria remains a serious concern in most African countries, causing nearly one millio... more Background Malaria remains a serious concern in most African countries, causing nearly one million deaths globally every year. The review aims to examine the extent and nature of the resurgence of malaria prevalence in South Africa.Method Using the Arksey and O'Malley framework, this scoping review included articles published between the year 2015 and 2018 on the resurgence of malaria prevalence in South Africa. Articles were searched from October 2018 to January 2019 using these electronic databases: CINAHL, Pubmed, Science Direct and SCOPUS. Grey literature from Google Scholar was also hand searched. Key search terms and subject headings such as climate variables; climate changes; climatic factors; malaria resurgence; malaria reoccurrence, and malaria increase over epidemic regions in South Africa were used to identify relevant articles. Articles for selection and characterization were performed by three independent reviewers. Data collected were synthesized qualitatively.Resu...
International Journal of Environmental Research and Public Health
Recent studies have considered the connections between malaria incidence and climate variables us... more 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 fr...
Introduction: The reasons for malaria resurgence mostly in Africa are yet to be well understood. ... more Introduction: The reasons for malaria resurgence mostly in Africa are yet to be well understood. Although the causes are often linked to regional climate change, it is important to understand the impact of climate variability on the dynamics of the disease. However, this is almost impossible without adequate long-term malaria data over the study areas. Methods: In this study, we develop a climate-based mosquito-human malaria model to study malaria dynamics in the human population over KwaZulu-Natal, one of the epidemic provinces in South Africa, from 1970-2005. We compare the model output with available observed monthly malaria cases over the province from September 1999 to December 2003. We further use the model outputs to explore the relationship between the climate variables (rainfall and temperature) and malaria incidence over the province using principal component analysis, wavelet power spectrum and wavelet coherence analysis. The model produces a reasonable fit with the obser...
The recent resurgence of malaria incidence across epidemic regions in South Africa has been linke... more 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 variabl...
The burden of arboviruses in the Americas is high and may result in long-term sequelae with infan... more The burden of arboviruses in the Americas is high and may result in long-term sequelae with infants disabled by Zika virus infection (ZIKV) and arthritis caused by infection with Chikungunya virus (CHIKV). We aimed to identify environmental drivers of arbovirus epidemics to predict where the next epidemics will occur and prioritize municipalities for vector control and eventual vaccination. We screened sera and urine samples (n = 10,459) from residents of 48 municipalities in the state of Rio de Janeiro for CHIKV, dengue virus (DENV), and ZIKV by molecular PCR diagnostics. Further, we assessed the spatial pattern of arbovirus incidence at the municipal and neighborhood scales and the timing of epidemics and major rainfall events. Lab-confirmed cases included 1,717 infections with ZIKV (43.8%) and 2,170 with CHIKV (55.4%) and only 29 (<1%) with DENV. ZIKV incidence was greater in neighborhoods with little access to municipal water infrastructure (r = -0.47, p = 1.2x10-8). CHIKV in...
Malaria is one of the most severe disease in the world. The projected climate change will probabl... more Malaria is one of the most severe disease in the world. The projected climate change will probably alter the region and transmission potential of malaria in Africa. In this study, a climate-based mathematical model to investigate the impact of temperature and rainfall on malaria transmission is developed and analysed. The basic reproduction number (R0) is derived along with stability analysis. The effect of the larval death rate on the reproduction number is also investigated. The model is validated on observed malaria transmission in Limpopo Province, South Africa, giving a reasonable fit and in particular, detecting accurately all the spikes in malaria prevalence. The model provides a numerical basis for further refinement towards prediction of the impact of climate variability on malaria transmission.
International Journal of Ecological Economics and Statistics, 2018
Cancer and hepatitis are global infectious diseases claiming millions of lives every year. In thi... more Cancer and hepatitis are global infectious diseases claiming millions of lives every year. In this study, we develop and analyse a cancer and hepatitis co-infection model to examine the relationship between the two diseases. In addition, we derive and analyse the reproduction numbers of each and both diseases. We explore the sensitivity of the reproduction numbers of both diseases to parameters, and performed the numerical simulations of the model. Our analyses show that the cancer-hepatitis model has disease-free equilibrium whenever the reproduction number is less than unity, and has a unique endemic equilibrium if the reproduction number is greater than one. Also our analyses highlight the impact of hepatitis on cancer and vise versa. Furthermore, our numerical solutions indicate that the carrying capacity and progression rate from susceptible to hepatitis and cancer play crucial roles in determining the population dynamics of both diseases. Although, our results highlight the im...
International Journal of Ecological Economics and Statistics, 2017
In this study, a comprehensive deterministic model for the transmission of malaria is develop and... more In this study, a comprehensive deterministic model for the transmission of malaria is develop and analyse. We consider in detailed the human population and both stages (aquatic and adult) of mosquito population are also included. We analyse the basic reproduction number, R0, and examine the existence and stability of equilibria. In an attempt to examine the effectiveness of control measure, we compute the sensitivity analysis of the reproductive number and highlight the importance of mosquito biting rate on malaria transmission. Finally, we perform the numerical simulations to verify the global stability of both diseasefree and endemic equilibrium.
A mathematical model for studying the impact of climate variability on malaria epidemics in South... more A mathematical model for studying the impact of climate variability on malaria epidemics in South Africa Gbenga Jacob Abiodun PhD thesis, Department of Mathematics and Applied Mathematics, Faculty of Natural Sciences, University of the Western Cape Malaria is most prevalent in tropical climates, where there are sufficient rainfall for mosquitoes to breed and conducive temperatures for both the mosquito and protozoa to live. A slight change in temperature can drastically affect the lifespan and patterns of mosquitoes, and moreover, the protozoan itself can only survive in a certain temperature range. With higher temperatures, mosquitoes can mature faster, and thus have more time to spread the disease. The malaria parasite also matures more quickly at warmer temperatures. However, if temperatures become too high, neither mosquitoes nor the malaria pathogen can survive. In addition, stagnant water is also a major contributor to the spread of malaria, since most mosquito species breed i...
In this study we revisit a theme of F. Nyabadza et al. [Journal of Biological Systems 18 No.2 (20... more In this study we revisit a theme of F. Nyabadza et al. [Journal of Biological Systems 18 No.2 (2010), 357–375], studying the optimal control of rolling out a public health education campaign and providing some further insights. We produce a Lyapunov function to prove global stability of the disease free equilibrium, and include a sensitivity analysis of the parameters and state variables. Finally, we present an optimal control problem on the implementation strategy of public health education, together with its solution. Various simulations are provided to illustrate the control problem.
The novel Coronavirus Disease 2019 (COVID-19) remains a worldwide threat to community health, soc... more The novel Coronavirus Disease 2019 (COVID-19) remains a worldwide threat to community health, social stability, and economic development. Since the first case was recorded on December 29, 2019, in Wuhan of China, the disease has rapidly extended to other nations of the world to claim many lives, especially in the USA, the United Kingdom, and Western Europe. To stay ahead of the curve consequent of the continued increase in case and mortality, predictive tools are needed to guide adequate response. Therefore, this study aims to determine the best predictive models and investigate the impact of lockdown policy on the USA’ COVID-19 incidence and mortality. This study focuses on the statistical modelling of the USA daily COVID-19 incidence and mortality cases based on some intuitive properties of the data such as overdispersion and autoregressive conditional heteroscedasticity. The impact of the lockdown policy on cases and mortality was assessed by comparing the USA incidence case with that of Sweden where there is no strict lockdown. Stochastic models based on negative binomial autoregressive conditional heteroscedasticity [NB INGARCH (p,q)], the negative binomial regression, the autoregressive integrated moving average model with exogenous variables (ARIMAX) and without exogenous variables (ARIMA) models of several orders are presented, to identify the best fitting model for the USA daily incidence cases. The performance of the optimal NB INGARCH model on daily incidence cases was compared with the optimal ARIMA model in terms of their Akaike Information Criteria (AIC). Also, the NB model, ARIMA model and without exogenous variables are formulated for USA daily COVID-19 death cases. It was observed that the incidence and mortality cases show statistically significant increasing trends over the study period. The USA daily COVID-19 incidence is autocorrelated, linear and contains a structural break but exhibits autoregressive conditional heteroscedasticity. Observed data are compared with the fitted data from the optimal models. The results further indicate that the NB INGARCH fits the observed incidence better than ARIMA while the NB models perform better than the optimal ARIMA and ARIMAX models for death counts in terms of AIC and root mean square error (RMSE). The results show a statistically significant relationship between the lockdown policy in the USA and incidence and death counts. This suggests the efficacy of the lockdown policy in the USA.
Understanding the regional impact of future climate change is one of the major global challenges ... more 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 an...
We present a compartmental model in ordinary differential equations of malaria disease transmissi... more We present a compartmental model in ordinary differential equations of malaria disease transmission, accommodating the effect of indoor residual spraying on the vector population. The model allows for influx of infected migrants into the host population and for outflow of recovered migrants. The system is shown to have positive solutions. In the special case of no infected immigrants, we prove global stability of the disease-free equilibrium. Existence of a unique endemic equilibrium point is also established for the case of positive influx of infected migrants. As a case study we consider the combined South African malaria region. Using data covering 31 years, we quantify the effect of malaria infected immigrants on the South African malaria region.
We present and analyze a stochastic differential equation (SDE) model for the population dynamics... more We present and analyze a stochastic differential equation (SDE) model for the population dynamics of a mosquito-borne infectious disease. We prove the solutions to be almost surely positive and global. We introduce a numerical invariant ${\mathcal{R}}$ R of the model with ${\mathcal{R}}<1$ R < 1 being a condition guaranteeing the almost sure stability of the disease-free equilibrium. We show that stochastic perturbations enhance the stability of the disease-free equilibrium of the underlying deterministic model. We illustrate the main stability theorem through simulations and show how to obtain interval estimates when making forward projections. We consulted a wide range of literature to find relevant numerical parameter values.
The connections between malaria incidence and climate variability have been studied in recent tim... more The connections between malaria incidence and climate variability have been studied in recent time using some mathematical and statistical models. Many of the statistical models in literature focused on time series approach based on Box–Jenkins methodology. However, fitting time series model based on the Box–Jenkins methodology may be challenging. Most malaria incidence data are count and are over-dispersed. In this study, negative binomial models were formulated for fitting malaria incidence in Akure—one of the epidemic cities in Nigeria. In particular, negative binomial models were formulated for each of the number of outpatient individuals, number of inpatient individuals and mortality count as a function of some climate variables. It was found that an increase in minimum temperature and relative humidity at lag 1 significantly increased the chance of malaria transmission and thereby leads to an increase in the number of inpatient and outpatient individuals, as well as the total number of malaria cases. The minimum temperature, rainfall amount and relative humidity of the study area have a significant impact on the increase of number of inpatient and outpatient individuals while mortality count depends on the total number of reported malaria cases. The findings from this study is to offer in-depth understanding on climate-malaria incidence linkages in Akure, Nigeria.
International Journal of Environmental Research and Public Health
This contribution aims to investigate the influence of monthly total rainfall variations on malar... more This contribution aims to investigate the influence of monthly total rainfall variations on malaria transmission in the Limpopo Province. For this purpose, monthly total rainfall was interpolated from daily rainfall data from weather stations. Annual and seasonal trends, as well as cross-correlation analyses, were performed on time series of monthly total rainfall and monthly malaria cases in five districts of Limpopo Province for the period of 1998 to 2017. The time series analysis indicated that an average of 629.5 mm of rainfall was received over the period of study. The rainfall has an annual variation of about 0.46%. Rainfall amount varied within the five districts, with the northeastern part receiving more rainfall. Spearman’s correlation analysis indicated that the total monthly rainfall with one to two months lagged effect is significant in malaria transmission across all the districts. The strongest correlation was noticed in Vhembe (r = 0.54; p-value = <0.001), Mopani (...
Communications in Statistics: Case Studies, Data Analysis and Applications
Abstract The impact of climate variability on malaria transmission cannot be overemphasized. The ... more Abstract 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.
Background Malaria remains a serious concern in most African countries, causing nearly one millio... more Background Malaria remains a serious concern in most African countries, causing nearly one million deaths globally every year. The review aims to examine the extent and nature of the resurgence of malaria prevalence in South Africa.Method Using the Arksey and O'Malley framework, this scoping review included articles published between the year 2015 and 2018 on the resurgence of malaria prevalence in South Africa. Articles were searched from October 2018 to January 2019 using these electronic databases: CINAHL, Pubmed, Science Direct and SCOPUS. Grey literature from Google Scholar was also hand searched. Key search terms and subject headings such as climate variables; climate changes; climatic factors; malaria resurgence; malaria reoccurrence, and malaria increase over epidemic regions in South Africa were used to identify relevant articles. Articles for selection and characterization were performed by three independent reviewers. Data collected were synthesized qualitatively.Resu...
International Journal of Environmental Research and Public Health
Recent studies have considered the connections between malaria incidence and climate variables us... more 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 fr...
Introduction: The reasons for malaria resurgence mostly in Africa are yet to be well understood. ... more Introduction: The reasons for malaria resurgence mostly in Africa are yet to be well understood. Although the causes are often linked to regional climate change, it is important to understand the impact of climate variability on the dynamics of the disease. However, this is almost impossible without adequate long-term malaria data over the study areas. Methods: In this study, we develop a climate-based mosquito-human malaria model to study malaria dynamics in the human population over KwaZulu-Natal, one of the epidemic provinces in South Africa, from 1970-2005. We compare the model output with available observed monthly malaria cases over the province from September 1999 to December 2003. We further use the model outputs to explore the relationship between the climate variables (rainfall and temperature) and malaria incidence over the province using principal component analysis, wavelet power spectrum and wavelet coherence analysis. The model produces a reasonable fit with the obser...
The recent resurgence of malaria incidence across epidemic regions in South Africa has been linke... more 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 variabl...
The burden of arboviruses in the Americas is high and may result in long-term sequelae with infan... more The burden of arboviruses in the Americas is high and may result in long-term sequelae with infants disabled by Zika virus infection (ZIKV) and arthritis caused by infection with Chikungunya virus (CHIKV). We aimed to identify environmental drivers of arbovirus epidemics to predict where the next epidemics will occur and prioritize municipalities for vector control and eventual vaccination. We screened sera and urine samples (n = 10,459) from residents of 48 municipalities in the state of Rio de Janeiro for CHIKV, dengue virus (DENV), and ZIKV by molecular PCR diagnostics. Further, we assessed the spatial pattern of arbovirus incidence at the municipal and neighborhood scales and the timing of epidemics and major rainfall events. Lab-confirmed cases included 1,717 infections with ZIKV (43.8%) and 2,170 with CHIKV (55.4%) and only 29 (<1%) with DENV. ZIKV incidence was greater in neighborhoods with little access to municipal water infrastructure (r = -0.47, p = 1.2x10-8). CHIKV in...
Malaria is one of the most severe disease in the world. The projected climate change will probabl... more Malaria is one of the most severe disease in the world. The projected climate change will probably alter the region and transmission potential of malaria in Africa. In this study, a climate-based mathematical model to investigate the impact of temperature and rainfall on malaria transmission is developed and analysed. The basic reproduction number (R0) is derived along with stability analysis. The effect of the larval death rate on the reproduction number is also investigated. The model is validated on observed malaria transmission in Limpopo Province, South Africa, giving a reasonable fit and in particular, detecting accurately all the spikes in malaria prevalence. The model provides a numerical basis for further refinement towards prediction of the impact of climate variability on malaria transmission.
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