COVID-19 Epidemic Dynamics and Population Projections from Early Days of Case Reporting in a 40 million population from Southern India
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Abstract
India reported its first COVID19 case on 30 January 2020. Since then the epidemic has taken different trajectories across different geographical locations in the country. This study explores the population aggregated trajectories of COVID19 susceptible, infected and recovered or dead cases in the south Indian state of Telangana with a population of approximately 40 million. Information on cases reported from March 2 to April 4 was collated from government records. The susceptible-infected-removed (SIR) model for the spread of an infectious disease was used. Transmission parameters were extracted from existing literature that has emerged over past weeks from other regions with similar population densities as Telangana. Optimisation algorithms were used to get basic reproduction rate for different phases of nonpharmaceutical interventions rolled by the government. Peak accumulation is projected towards end of July with 36% of the population being infected by August 2020 if the population lockdown or social distancing mechanism is not continued. The number of deaths assuming no intervention is projected to be 488000 (95% CI: (329400, 646600)). A draconian enforcement of population lockdown combined with hand and face hygiene adherence would reduce the transmission by at least 99.7% whereas partial social distancing and hygiene would reduce it by 51.2%. Transmission parameters reported should be interpreted with caution as they are population aggregated and do not consider unique characteristics of susceptibility among micro-clusters and vulnerable individuals. More data will need to be collected to optimize transmission parameters and evaluate the full complexity, to simulate real world scenarios in the models.
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SciScore for 10.1101/2020.04.17.20070292: (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: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:One of the limitations of this work is the lack of discrimination between urban and rural areas. This was deliberate as at the time of collating data, reports stratified by level of urbanization were not very reliable. However, the possibility of the infection/epidemic already moving beyond the city perimeter to other districts or rural parts of the state cannot be ignored. It is well known that with the availability of new information, in …
SciScore for 10.1101/2020.04.17.20070292: (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: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:One of the limitations of this work is the lack of discrimination between urban and rural areas. This was deliberate as at the time of collating data, reports stratified by level of urbanization were not very reliable. However, the possibility of the infection/epidemic already moving beyond the city perimeter to other districts or rural parts of the state cannot be ignored. It is well known that with the availability of new information, in recent years, the country changed epidemic estimates for other epidemics like HIV and TB [29,30]. Also historical experiences from earlier outbreaks [31 should be combined with new estimates to inform effective interventions. Any scientific estimation needs robust local data. COVID-19 is new, and as one moves in time, more evidence will be available for better estimations. The authors would like to emphasise that, these are population level projections. The inherent assumptions will not address micro clusters such as health workers, the modelling does not adjust for vulnerable groups and loci that may be high risk locations such as hospitals. At the present time more data is needed to clearly understand the differential transmission dynamics in special groups. The model does not have the ability to project precisely what may happen after the lockdown is lifted, previous experience with the 1918 Influenza pandemic [31] suggests that many different possibilities exist. Measures such as lockdown are considered as drastic public health measures...
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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