An agent based model for assessing transmission dynamics and health systems burden for COVID-19
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Abstract
<span>Coronavirus disease of 2019 </span><span>(COVID-19) pandemic has caused over <br /> 230 million infections with more than 4 million deaths worldwide. Researches have been using various mathematical and simulation techniques to estimate the future trends of the pandemic to help the policymakers and healthcare fraternity. Agent-based models (ABM) could provide accurate projections than the compartmental models that have been largely used. The present study involves a simulation of ABM using a synthetic population from India to analyze the effects of interventions on the spread of the disease. A disease model with various states representing the possible progression of the disease was developed and simulated using AnyLogic. The results indicated that imposing stricter non-pharmaceutical interventions (NPI) lowered the peak values of infections, the proportion of critical patients, and the deceased. Stricter interventions offer a larger time window for the healthcare fraternity to enhance preparedness. The findings of this research could act as a start-point to understand the benefits of ABM-based models for projecting infectious diseases and analyzing the effects of NPI imposed.</span>
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SciScore for 10.1101/2020.06.04.20121848: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Exploring the contact network and dynamics of different regions would help us to represent the region-specific disease spread better [10] There are certain limitations to the study as parameters such as underlying health conditions, migration routes, adoption of control measures (face mask, social distancing, etc.), longitudinally …
SciScore for 10.1101/2020.06.04.20121848: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Exploring the contact network and dynamics of different regions would help us to represent the region-specific disease spread better [10] There are certain limitations to the study as parameters such as underlying health conditions, migration routes, adoption of control measures (face mask, social distancing, etc.), longitudinally varying lockdown phases, etc., have not been considered. The parameters which were used in the model were form different countries and may not represent the India or Rangareddy district scenario. The results of simulation model clearly indicate that the peak values could significantly be reduced by increasing the lockdown imposed. Thus, the importance of reducing the number of contacts, i.e., social distancing, is apparent through the results of this study and flattening the disease curve.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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