COVID-19
Kyosuke Ono
Abstract
Background: A mathematical investigation of the reasons for the fifth wave's quick expansion and reduction in Tokyo, Japan, is required to avoid the spread of subsequent COVID-19 infections. Methods: Using the simple IR theory underlying the susceptible-infectious-removed (SIR) hypothesis of infectious ...
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Background: A mathematical investigation of the reasons for the fifth wave's quick expansion and reduction in Tokyo, Japan, is required to avoid the spread of subsequent COVID-19 infections. Methods: Using the simple IR theory underlying the susceptible-infectious-removed (SIR) hypothesis of infectious disease epidemics, infected persons (I), infection rate, and testing/isolation rate are determined from accessible data of daily positive cases (R) and testing numbers. Results: The rapid spread of illness from late July to mid-August was owing to a drop in the number of people tested to half that of weekdays during the Olympic Games' four and three-day vacations. The maximum number of daily positives would have been lowered to two-fifths of the actual positives in early August if the number of weekday tests had been maintained during these holidays and would have fallen monotonically thereafter. The infection rates mean value fell steadily from 0.65 in late August to around 0.25 by the end of September. The significant increase in vaccination rates is mostly to blame for the fall in infection rates. In Tokyo, the impact of mRNA-based vaccines on infection prevention and increased vaccination rates could reduce the infection rate to 1/2 on September 10 and 1/3 by the end of October. Conclusion: According to the findings, a new infection like the delta variant can be suppressed to less than the fifth wave by increasing vaccination rates, eliminating three consecutive holidays, and implementing a precautionary testing system that maintains the same number of tests on weekends as on weekdays in the event of a rapid spread of infection in an emergency.
COVID-19
Virendra Mane; Poorva Bhalerao
Abstract
Background: The COVID-19 pandemic was expected to affect India severely; cases rose exponentially from May-June 2020, but around mid-September reached their peak and started declining. It showed a sign of the wave’s completion by the end of January 2021. This decline was not predicted by any models ...
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Background: The COVID-19 pandemic was expected to affect India severely; cases rose exponentially from May-June 2020, but around mid-September reached their peak and started declining. It showed a sign of the wave’s completion by the end of January 2021. This decline was not predicted by any models and the authors have not come across any explanation. Winter seasonality of influenza and similar viruses is well known and observed fact and that it has a direct correlation to the colder temperatures as well as lower humidity. Similarly, in low humidity, viruses are most viable, and they become ineffective as the humidity increases and reaches its maximum extent. This article hypothesizes and tries to explain the cause behind the first major decline and shows the subsequent rise of the second wave, and one short low humidity period followed by a high humidity period between the first and second waves. Methods: The humidity cycles in India were studied to find high and low relative humidity periods, which then corresponded to the daily cases in the country (macro-level), region (mid-level), and smaller regions (micro-level). Results: A definite correlation was observed between Monsoon-induced humidity and the incidence rate decline. This happens in 8 to 10 weeks. Incidence rates start declining about 4 weeks after the peak humidity is reached in a particular region. A decrease in humidity below 65% or 55% or lower causes an increase in the case increase/uptrend in about 3-4 weeks. Conclusion: COVID-19 has a seasonal peak in India, peaking in the middle of the monsoon season around mid-September and reaching its lowest levels in January-February. As humidity drops from February to June/July, a trend reversal and sharp rise are expected. The subsequent wave/case peak would be expected to be seen around mid-September 2021.
