An Agent-Based Model to assess COVID-19 spread and health systems burden in Telangana state, India
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Abstract
Objectives
To assess the transmission dynamics and the health systems’ burden of COVID-19 using an Agent Based Modeling (ABM) approach using a synthetic population.
Study design
The study used a synthetic population with 31,738,240 agents representing 90.67 percent of the overall population of Telangana state, India as per 2011 Census of India. Lockdown phases as per Indian scenario considering the effects of post-lockdown, use of control measures and immunity on secondary infections were studied.
Methods
The counts of people in different health states were measured separately for each district of Telangana. The model was run for 365 days and six scenarios with varying proportions of people using control measures (100%, 75% and 50%) and varying immunity periods (90 and 180 days). Sensitivity Analysis has been done for two districts to compare the change in transmission dynamics when incubation period and asymptomatic proportion are changed.
Results
Results indicate that the peak values were attained soon after the lockdown was lifted. The risk estimates indicate that protection factor values are higher when more proportion of people adopt control measures.
Conclusions
ABM approach helps to analyze grassroot details compared to compartmental models. Risk estimates allow the policymakers to determine the protection offered, its strength and percentage of population shielded by use of control measures.
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SciScore for 10.1101/2020.10.03.20206177: (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
Software and Algorithms Sentences Resources Model simulation: The model coded in Python was equipped with the input parameters and run for six different scenarios (table 4) [18]. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations to the study include the exclusion of comorbidities among patients, transportation modes, indirect transmission through suspended particles, etc., which could be considered to improve the accuracy of the model. Considering more …
SciScore for 10.1101/2020.10.03.20206177: (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
Software and Algorithms Sentences Resources Model simulation: The model coded in Python was equipped with the input parameters and run for six different scenarios (table 4) [18]. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations to the study include the exclusion of comorbidities among patients, transportation modes, indirect transmission through suspended particles, etc., which could be considered to improve the accuracy of the model. Considering more parameters are however limited to the availability and authenticity of data.
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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