Epidemic prediction of dengue fever based on vector compartment model and Markov chain Monte Carlo method

Background Dengue epidemics is affected by vector-human interactive dynamics. Infectious disease prevention and control emphasize the timing intervention at the right diffusion phase. In such a way, control measures can be cost-effective, and epidemic incidents can be controlled before devastated consequence occurs. However, timing relations between a measurable signal and the onset of the pandemic are complex to be discovered, and the typical lag period regression is difficult to capture in these complex relations. This study investigates the dynamic diffusion pattern of the disease in terms of a probability distribution. We estimate the parameters of an epidemic compartment model with the cross-infection of patients and mosquitoes in various infection cycles. We comprehensively study the incorporated meteorological and mosquito factors that may affect the epidemic of dengue fever to predict dengue fever epidemics. Results We develop a dual-parameter estimation algorithm for a composite model of the partial differential equations for vector-susceptible-infectious-recovered with exogeneity compartment model, Markov chain Montel Carlo method, and boundary element method to evaluate the epidemic periodicity under the effect of environmental factors of dengue fever, given the time series data of 2000–2016 from three cities with a population of 4.7 million. The established computer model of “energy accumulation-delayed diffusion-epidemics” is proven to be effective to predict the future trend of reported and unreported infected incidents. Our artificial intelligent algorithm can inform the authority to cease the larvae at the highest vector infection time. We find that the estimated dengue report rate is about 20%, which is close to the number of official announcements, and the percentage of infected vectors increases exponentially yearly. We suggest that the executive authorities should seriously consider the accumulated effect among infected populations. This established epidemic prediction model of dengue fever can be used to simulate and evaluate the best time to prevent and control dengue fever. Conclusions Given our developed model, government epidemic prevention teams can apply this platform before they physically carry out the prevention work. The optimal suggestions from these models can be promptly accommodated when real-time data have been continuously corrected from clinics and related agents.

Covid-19 Epidemic Prediction in France : the Multimodal Case

In two previous papers we have proposed models to estimate the Covid-19 epidemic when the number of daily positive cases has a bell shaped form that we call a mode. We have observed that each Covid variant produces this type of epidemic shape at a different moment, resulting in a multimodal epidemic shape. We will show in this document that each mode can still be estimated with models described in the two previous papers provides we replace the cumulated number of positive cases y by the cumulated number of positive cases reduced by a parameter P to be estimated. Therefore denoting z the logarithm of y −P, z follows approximately the differential equation ż = b −azr where a, b, r have also to be estimated from the observed data. We will show the obtained predictions on the four French modes April, November 2020, May and September 2021. The comparison between the prediction obtained before the containment decisions made by the French government and the observed data afterwards suggests the inefficiency of the epidemic lockdowns.

Analysis and Management of COVID-19 Using Computational Intelligence Technologies

After the outbreak of COVID-19, the world economy and people’s health have been greatly challenged. What is the law of the spread of COVID-19, when will it reach its peak, and when will it be effectively controlled? These have all become major issues of common concern throughout China and the world. Based on this background, this article introduces a variety of classic computational intelligence technologies to predict the spread of COVID-19. Computational intelligence technology mainly includes support vector machine regression (SVR), Takagi-Sugeuo-Kang fuzzy system (TSK-FS), and extreme learning machine (ELM). Compare the predictions of the infection rate, mortality rate, and recovery rate of the COVID-19 epidemic in China by each intelligent model in 5 and 10 days, the effectiveness of the computational intelligence algorithm used in epidemic prediction is verified. Based on the prediction results, the patients are classified and managed. According to the time of illness, physical fitness and other factors, patients are divided into three categories: Severe, moderate, and mild. In the case of serious shortage of medical equipment and medical staff, auxiliary medical institutions take corresponding treatment measures for different patients.