arXiv subjects

Indranil Mukhopadhyay

Publications and source records attributed to Indranil Mukhopadhyay.

5 recordsLinked to original sources

arXiv2026

An AI-Enabled Agent-Based Simulation Platform for Studying COVID-19 Pandemic

Understanding outbreak dynamics is essential for designing effective control measures. We developed an agent-based model to examine how changes in epidemiological and intervention parameters affect infection progression in a synthetic population. The model incorporates individual demographic characteristics, including age, sex, and working status, as well as the number and location of infection epicentres, diagnostic sensitivity, the proportion of asymptomatic infections, and the timing and duration of lockdowns. By tracking each individual, the simulator characterizes infection progression through a community over time. In a closed population of 10000 people, cases peaked around the sixth week and declined by approximately the fifteenth week in the absence of lockdown. When primary cases were introduced within densely populated clusters, cases peaked earlier and declined more slowly. Lockdowns delayed and reduced the infection peak, whereas lower diagnostic sensitivity increased cases and deaths. The number of cases decreased as the proportion of asymptomatic infections increased under the model's assumptions. The model produces reproducible estimates under realistic parameter settings and can accommodate factors such as infectivity period, testing yield, socioeconomic status, daily travel, awareness, population density, and social distancing. It can also be adapted to infections with similar transmission dynamics. The model is available as an open, interactive web application that enables users without programming experience to design scenarios and examine outbreak dynamics in real time. Beyond forecasting, the simulator provides a reusable in-silico environment, or digital twin, for synthetic-data generation and AI-assisted optimization of intervention policies.

q-bio.PE↗

arXiv2021

Querying multiple sets of $p$-values through composed hypothesis testing

Motivation: Combining the results of different experiments to exhibit complex patterns or to improve statistical power is a typical aim of data integration. The starting point of the statistical analysis often comes as sets of p-values resulting from previous analyses, that need to be combined in a flexible way to explore complex hypotheses, while guaranteeing a low proportion of false discoveries. Results: We introduce the generic concept of composed hypothesis, which corresponds to an arbitrary complex combination of simple hypotheses. We rephrase the problem of testing a composed hypothesis as a classification task, and show that finding items for which the composed null hypothesis is rejected boils down to fitting a mixture model and classify the items according to their posterior probabilities. We show that inference can be efficiently performed and provide a thorough classification rule to control for type I error. The performance and the usefulness of the approach are illustrated on simulations and on two different applications. The method is scalable, does not require any parameter tuning, and provided valuable biological insight on the considered application cases. Availability: The QCH methodology is implemented in the qch R package hosted on CRAN.

stat.ME↗

arXiv2020

Critical community size for COVID-19 -- a model based approach to provide a rationale behind the lockdown

Background: Restrictive mass quarantine or lockdown has been implemented as the most important controlling measure to fight against COVID-19. Many countries have enforced 2 - 4 weeks' lockdown and are extending the period depending on their current disease scenario. Most probably the 14-day period of estimated communicability of COVID-19 prompted such decision. But the idea that, if the susceptible population drops below certain threshold, the infection would naturally die out in small communities after a fixed time (following the outbreak), unless the disease is reintroduced from outside, was proposed by Bartlett in 1957. This threshold was termed as Critical Community Size (CCS). Methods: We propose an SEIR model that explains COVID-19 disease dynamics. Using our model, we have calculated country-specific expected time to extinction (TTE) and CCS that would essentially determine the ideal number of lockdown days required and size of quarantined population. Findings: With the given country-wise rates of death, recovery and other parameters, we have identified that, if at a place the total number of susceptible population drops below CCS, infection will cease to exist after a period of TTE days, unless it is introduced from outside. But the disease will almost die out much sooner. We have calculated the country-specific estimate of the ideal number of lockdown days. Thus, smaller lockdown phase is sufficient to contain COVID-19. On a cautionary note, our model indicates another rise in infection almost a year later but on a lesser magnitude.

q-bio.PE↗

arXiv2018

Statistical Validity and Consistency of Big Data Analytics: A General Framework

Informatics and technological advancements have triggered generation of huge volume of data with varied complexity in its management and analysis. Big Data analytics is the practice of revealing hidden aspects of such data and making inferences from it. Although storage, retrieval and management of Big Data seem possible through efficient algorithm and system development, concern about statistical consistency remains to be addressed in view of its specific characteristics. Since Big Data does not conform to standard analytics, we need proper modification of the existing statistical theory and tools. Here we propose, with illustrations, a general statistical framework and an algorithmic principle for Big Data analytics that ensure statistical accuracy of the conclusions. The proposed framework has the potential to push forward advancement of Big Data analytics in the right direction. The partition-repetition approach proposed here is broad enough to encompass all practical data analytic problems.

cs.DB↗

arXiv2017

New Empirical Evidence on Disjunction Effect and Cultural Dependence

We perform new experiment using almost the same sample size considered by Tversky and Shafir to test the validity of classical probability theory in decision making. The results clearly indicate that the disjunction effect depends also on culture and more specifically on gender (females rather than males). We did more statistical analysis rather that putting the actual values done by previous authors. We propose different kind of disjunction effect i.e. strong and weak based on our statistical analysis.

q-bio.NC↗