Global Stability Analysis of the Mathematical Model for Malaria Transmission between Vector and Host Population

In this paper, we discuss a mathematical model of malaria transmission between vector and host population. We study the basic qualitative properties of the model, the boundedness and non-negativity, calculate all equilibria, and prove the global stability of them and the behaviour of the model when the basic reproduction ratio R0 is greater than one or less than one. The global stability of equilibria is established by using Lyapunov method. Graphical representations of the calculated parameters and their effects on disease eradication are provided.

Modeling the Influence of Climate Factors on Malaria Transmission Dynamics in North Kordofan State, Sudan

Background: Despite great efforts by the government to control malaria in Sudan, the disease is the most significant human disease and was widespread in North Kordofan State. Morbidity and mortality of the disease are increasing in the State. Usually, the disease reached its peak after rainy season. This study aims to estimate the role of climate factors on malaria transmission dynamic by modeling the relationship between malaria cases and climatic variables, such as rainfall, relative humidity, and temperature, in Kordofan State. Methods: We used Pearson correlation coefficient and an ordinary least square method to assess this relationship. Results: The results show that there are statistically significant associations between malaria cases and rainfall, relative humidity, and minimum temperature (P-value < 0.001). The regression analysis results suggest that the appropriate model for predicting malaria incidence includes malaria cases lagged by one month, maximum temperature, and minimum temperature. This model explained 72% of the variance in monthly malaria incidence. Conclusion: The results of this study suggest that climatic factors have potential use for malaria prediction in the State.

Systematized Literature Review on Spatial Analysis of Environmental Risk Factors of Malaria Transmission

Malaria is still the major parasitic disease in the world, with approximately 438,000 deaths in 2015. Environmental risk factors (ERF) have been widely studied, however, there are discrepancies in the results about their influence on malaria transmission. Recently, papers have been published about geospatial analysis of ERF of malaria to explain why malaria varies from place to place. Our primary objective was to identify the environmental variables most used in the geospatial analysis of malaria transmission. The secondary objective was to identify the geo-analytic methods and techniques, as well as geo-analytic statistics commonly related to ERF and malaria. We conducted a systematized review of articles published from January 2004 to March 2015, within Web of Science, Pubmed and LILACS databases. Initially 676 articles were found, after inclusion and exclusion criteria, 29 manuscripts were selected. Temperature, land use and land cover, surface moisture and vector breeding site were the most frequent included variables. As for geo-analytic methods, geostatistical models with Bayesian framework were the most applied. Kriging interpolations, Geographical Weighted Regression as well as Kulldorff’s spatial scan were the techniques more widely used. The main objective of many of these studies was to use these methods and techniques to create malaria risk maps. Spatial analysis performed with satellite images and georeferenced data are increasing in relevance due to the use of remote sensing and Geographic Information System. The combination of these new technologies identifies ERF more accurately, and the use of Bayesian geostatistical models allows a wide diffusion of malaria risk maps. It is known that temperature, humidity vegetation and vector breeding site play a critical role in malaria transmission;however, other environmental risk factors have also been identified. Risk maps have a tremendous potential to enhance the effectiveness of malaria-control programs.

SEIRS model for malaria transmission dynamics incorporating seasonality and awareness campaign

Malaria,a devastating disease caused by the Plasmodium parasite and transmitted through the bites of female Anopheles mosquitoes,remains a significant public health concern,claiming over 600,000 lives annually,predominantly among children.Novel tools,including the application of Wolbachia,are being developed to combat malaria-transmitting mosquitoes.This study presents a modified susceptible-exposed-infectious-recovered-susceptible(SEIRS)compartmental mathematical model to evaluate the impact of awareness-based control measures on malaria transmission dynamics,incorporating mosquito interactions and seasonality.Employing the next-generation matrix approach,we calculated a basic reproduction number(R0)of 2.4537,indicating that without robust control measures,the disease will persist in the human population.The model equations were solved numerically using fourth and fifth-order Runge-Kutta methods.The model was fitted to malaria incidence data from Kenya spanning 2000 to 2021 using least squares curve fitting.The fitting algorithm yielded a mean absolute error(MAE)of 2.6463 when comparing the actual data points to the simulated values of infectious human population(Ih).This finding indicates that the proposed mathematical model closely aligns with the recorded malaria incidence data.The optimal values of the model parameters were estimated from the fitting algorithm,and future malaria dynamics were projected for the next decade.The research findings suggest that social media-based awareness campaigns,coupled with specific optimization control measures and effective management methods,offer the most cost-effective approach to managing malaria.

Accessing the syndemic of COVID-19 and malaria in tervention in Africa

Background:The pandemic of the coronavirus disease 2019(COVID-19)has caused substantial disruptions to health services in the low and middle-income countries with a high burden of other diseases,such as malaria in sub-Saharan Africa.The aim of this study is to assess the impact of COVID-19 pandemic on malaria transmission potential in malaria-endemic countries in Africa.Methods:We present a data-driven method to quantify the extent to which the COVID-19 pandemic,as well as various non-pharmaceutical interventions(NPIs),could lead to the change of malaria transmission potential in 2020.First,we adopt a particle Markov Chain Monte Carlo method to estimate epidemiological parameters in each country by fitting the time series of the cumulative number of reported COVID-19 cases.Then,we simulate the epidemic dynamics of COVID-19 under two groups of NPIs:(1)contact restriction and social distanci ng,and(2)early ide ratification and isolation of cases.Based on the simulated epidemic curves,we quantify the impact of COVID-19 epidemic and NPIs on the distribution of insecticide-treated nets(ITNs).Finally,by treating the total number of ITNs available in each country in 2020,we evaluate the negative effects of COVID-19 pandemic on malaria transmission potential based on the notion of vectorial capacity.Results:We con duct case studies in four malaria-endemic coun tries,Ethiopia,Nigeria,Tanza nia,and Zambia,in Africa.The epidemiological parameters(i.e.;the basic reproduction number R°and the duration of infection D1)of COVID-19 in each country are estimated as follows:Ethiopia(Rq=1.57,D1=5.32),Nigeria(Ro=2.18,D1=6.58),Tanzania(Ro=2.47,D1=6.01),and Zambia(R0=2.12,D1=6.96).Based on the estimated epidemiological parameters,the epidemic curves simulated under various NPIs indicated that the earlier the interventions are implemented,the better the epidemic is controlled.Moreover,the effect of combined NPIs is better than contact restriction and social di st a ncing only.By treating the total number of ITNs available in each country in 2020 as a baseline,our results show that even with stringent NPIs,malaria transmission potential will remain higher than expected in the second half of 2020.Conclusions:By quantifying the impact of various NPI response to the COVID-19 pandemic on malaria transmission potential,this study provides a way tojointly address the syndemic between COVID-19 and malaria in malariaendemic countries in Africa.The results suggest that the early intervention of COVID-19 can effectively reduce the scale of the epidemic and mitigate its impact on malaria transmission potential.

Cross-sectional prevalence and pattern of non-anaemia severe malaria among 2–10 year olds in Sokoto in Northwestern Nigeria

Malaria is highly prevalent in Nigeria and accounts for approximately 40%of global malaria mortality.However,most reports on severe malaria in Nigeria are from hospital-based studies without accurate information from communities;thus,malaria-related deaths in the community are left untracked.This study aimed to describe the prevalence and pattern of severe malaria in a community in Northwestern Nigeria.A cross-sectional study was conducted among 2–10-year-old children in Sokoto,in August and December 2016,to determine the endemicity of malaria based on Plasmodium falciparum prevalence rate(PfPR2-10)and to describe the disease pattern.Severe malaria was diagnosed according to the World Health Organisation criteria.Data were described using Stata version 15.The prevalence of non-anaemia severe malaria was higher than expected(2.6%),considering the endemicity pattern which was mesoendemic based on a PfPR2-10 of 34.8%.The mean age of children with severe malaria was 3.73 years,and the male—female ratio was 2:1.However,54.0%of the patients had hyperparasitaemia.A relatively high prevalence of non-anaemia severe malaria was found in Wamakko.This finding suggests the need to identify and treat cases in the community using modifications of current strategies,particularly seasonal malaria chemoprophylaxis.