A Fuzzy Rule-Based Compartmental Modeling for SARS-CoV-2

Different epidemiological compartmental models have been presented to predict the transmission dynamics of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is the most burning issue all over the world right now. In this study, we have proposed a new fuzzy rule-based Susceptible-Exposed-Infected-Recovered-Death (SEIRD) compartmental model to delineate the intervention and transmission heterogeneity in China, New Zealand, United States and Bangladesh for SARS-CoV-2 viral infection. We have introduced a new dynamic fuzzy transmission possibility variable in the compartmental model. Through our model, we have presented the correspondence of the intervention measures in relaxing the transmission possibility. We estimated that the peak in the US might arrive during the last half of August and for Bangladesh, it might occur during the first half of August, 2020 if current intervention measures are not violated. We have modeled a prediction scenario for Bangladesh if current intervention measures are violated due to Eid-ul-Azha. We further investigated what might happen if Bangladesh government reopens everything from September, 2020. We suggested various effective epidemic control policies for the authority of Bangladesh to fight against the virus. We concluded analyzing the current scenario of Bangladesh suggesting that extensive tests must be carried out collecting more samples of the asymptomatic individuals along with the symptomatic cases and also proper isolation and quarantine measures should be maintained strictly to contain the epidemic sooner.

Pareto Type-II Model under Type-I Progressive Hybrid Censoring: Bound Lengths

The Pareto Type-II model is considered here from which, the observable is to be predicted by using Bayesian approach. The Bayes prediction bound lengths are obtained for Type-I progressive hybrid censored data. Both One-sample and Two-sample Bayes prediction scenario has included in the present study. Both known and unknown cases of the scale parameter have considered in the present study. A comparison also has made with the asymptotic interval estimates, are made-up from the Fisher information matrix. Performance of the different methods has studied by simulation and a real data set.

Malaria Morbidity Prediction Scenario in Indonesia

Governments commitment in eradicating malaria has been realized in Malaria elimination program. The program aims to reduce Malaria case to zero in 2030. Starting from 2011, Indonesia suffered a drop in APIs value from 1,75 to 0,84. Despite the numerous drop in Malaria cases, some regions are still suffering from large major outbreaks especially in eastern Indonesia. WHO declares that Indonesia is a country at risk of malaria, because of the high rates of malaria morbidity. The aims of this paper is to predict the trend of malaria morbidity with the API variable value of each province in Indonesia. The method used in this research is probabilistic method using extrapolation trends and ARIMA (Autoregressive Integrated Moving Average) using variation percentage of training and testing data to obtain the best prediction method. Result of this article is API value scenario in Indonesia up to 2030. Based on the analysis result, the best method to predict the value of API is exponential growth method because it has the smallest MAPE value, which is 38.48 using 80% training data and 20% testing data. The prediction results show that from year 2018 to 2030, the value of API will decrease from 0.45 to 0.016.