ABSTRACT
BackgroundThe COVID-19 pandemic, that has resulted in millions of deaths and hundreds of millions of cases worldwide, continues to affect the lives, health and economy of various countries including Bangladesh. Despite the high proportion of asymptomatic cases and relatively low mortality, the viruss spread had been a significant public health problem for densely populated Bangladesh. With the healthcare system at stress, understanding the disease dynamics in the unique Bangladesh context became essential to guide policy decisions. MethodsWith a goal to capture the COVID-19 disease dynamics, we developed two stochastic Agent-Based Models (ABMs) considering the key characteristics of COVID-19 in Bangladesh, which vastly differ from the developed countries. We have implemented our ABMs extending the popular (but often inadequate) SIR model, where the infected population is sub-divided into Asymptomatic, Mild Symptomatic and Severe Symptomatic populations. One crucial issue in Bangladesh is the lack of enough COVID-19 tests as well as unwillingness of people to do the tests resulting in much less number of official positive cases than the actual reality. Although not directly relevant to the epidemiological process, our model attempts to capture this crucial aspect while calibrating against official daily test-positive cases. Our first model, ABM-BD, divides the population into age-groups that interact among themselves based on an aggregated Contact Matrix. Thus ABM-BD considers aggregate agents and avoids direct agent level interactions as the number of agents are prohibitively large in our context. We also implement a scaled down model, ABM-SD, that is capable of simulating agent level interactions. ResultsABM-BD was quite well-calibrated for Dhaka: the Mean Absolute Percentage Error (MAPE) between official and forecasted cases was 1.845 approximately during the period between April 4, 2020 and March 31, 2021. After an initial model validation, we conducted a number of experiments - including retrospective scenario analysis, and hypothetical future scenario analysis. For example, ABM-BD has demonstrated the trade off between a strict lockdown with low infections and a relaxed lockdown with reduced burden on the economy. Leveraging the true agent level interaction capability of ABD-SD, we have also successfully analyzed the relative severity of different strains thereby (confidently) capturing the effect of different virus mutations. ConclusionsOur models have adequately captured the COVID-19 disease transmission dynamics in Bangladesh. This is a useful tool to forecast the impact of interventions to assist policymakers in planning appropriate COVID response. Our models will be particularly useful in a resource constrained setting in countries like Bangladesh where the population size is huge.
ABSTRACT
The Coronavirus disease 2019 (COVID-19) has resulted in an ongoing pandemic worldwide. Countries have adopted Non-pharmaceutical Interventions (NPI) to slow down the spread. This study proposes an Agent Based Model that simulates the spread of COVID-19 among the inhabitants of a city. The Agent Based Model can be accommodated for any location by integrating parameters specific to the city. The simulation gives the number of daily confirmed cases. Considering each person as an agent susceptible to COVID-19, the model causes infected individuals to transmit the disease via various actions performed every hour. The model is validated by comparing the simulation to the real data of Ford county, Kansas, USA. Different interventions including contact tracing are applied on a scaled down version of New York city, USA and the parameters that lead to a controlled epidemic are determined. Our experiments suggest that contact tracing via smartphones with more than 60% of the population owning a smartphone combined with a city-wide lock-down results in the effective reproduction number (Rt) to fall below 1 within three weeks of intervention. In the case of 75% or more smartphone users, new infections are eliminated and the spread is contained within three months of intervention. Contact tracing accompanied with early lock-down can suppress the epidemic growth of COVID-19 completely with sufficient smartphone owners. In places where it is difficult to ensure a high percentage of smartphone ownership, tracing only emergency service providers during a lock-down can go a long way to contain the spread. No particular funding was available for this project.
ABSTRACT
BackgroundCovid-19 pandemic, caused by the SARS-CoV-2 genome sequence of coronavirus, has affected millions of people all over the world and taken thousands of lives. It is of utmost importance that the character of this deadly virus be studied and its nature be analyzed. MethodsWe present here an analysis pipeline comprising a classification exercise to identify the virulence of the genome sequences and extraction of important features from its genetic material that are used subsequently to predict mutation at those interesting sites using deep learning techniques. ResultsWe have classified the SARS-CoV-2 genome sequences with high accuracy and predicted the mutations in the sites of Interest. ConclusionsIn a nutshell, we have prepared an analysis pipeline for hCov genome sequences leveraging the power of machine intelligence and uncovered what remained apparently shrouded by raw data.
ABSTRACT
The COVID-19 epidemic had spread rapidly through China and subsequently has proliferated globally leading to a pandemic situation around the globe. Human-to-human transmissions, as well as asymptomatic transmissions of the infection, have been confirmed. As of April 3rd public health crisis in China due to COVID-19 is potentially under control. We compiled a daily dataset of case counts, mortality, recovery, temperature, population density, and demographic information for each prefecture during the period of January 11 to April 07, 2020 (excluding Wuhan from our analysis due to missing data). Understanding the characteristics of spatiotemporal clustering of the COVID-19 epidemic and R0 is critical in effectively preventing and controlling the ongoing global pandemic. The prefectures were grouped based on several relevant features using unsupervised machine learning techniques. We performed a computational analysis utilizing the reported cases in China to estimate the revised R0 among different regions for prevention planning in an ongoing global pandemic. Finally, our results indicate that the impact of temperature and demographic (different age group percentage compared to the total population) factors on virus transmission may be characterized using a stochastic transmission model. Such predictions will help prioritize segments of a given community/region for action and provide a visual aid in designing prevention strategies for a specific geographic region. Furthermore, revised estimation and our methodology will aid in improving the human health consequences of COVID-19 elsewhere.
