 COVID-19 has infected more than 823,000 people globally and resulted in over 40,000 deaths as of April 1, 2020. Swift government response to contain the outbreak requires accurate and continuous census of the infected population, particularly with regards to viral carriers without severe symptoms. We take on this task by converting the symptom onset time distribution, which we calibrated , into the percentage of the latent, pre-symptomatic and symptomatic groups through a novel mathematical procedure . We then estimate the reduction of the basic reproduction number R_0 under specific disease control practices such as contact tracing, testing, social distancing, wearing masks and staying at home . When these measures are implemented in parallel , their effects on R_0 multiply. For example, if 70\% of the general public wear masks and contact tracing is conducted at 60\% efficiency within a 4-day time frame, epidemic growth will be flattened in the hardest hit countries. Finally, we analyze the bell-shaped curves of epidemic evolution from various affected regions and point out the significance of a universal decay rate of -0.32/day in the final eradication of the disease .
Within a short period of time, COVID-19 grew into a world-wide pandemic. Transmission by pre-symptomatic and asymptomatic viral carriers rendered intervention and containment of the disease extremely challenging. Based on reported infection case studies, we construct an epidemiological model that focuses on transmission around the symptom onset . The model is calibrated against incubation period and serial interval statistics during early stages of the pandemic. Mathematical treatment of the model yields explicit expressions for the size of latent and pre-symptomatic subpopulations during the exponential growth phase . We then explore reduction of the basic reproduction number R_0 through specific disease control measures such as contact tracing, testing, social distancing, wearing masks and sheltering in place . When these measures are implemented in combination , their effects on R_0 multiply. We also compare our model behaviour to pandemic evolution data from various affected regions and highlight generic and less generic features of COVID-19 pandemic development .
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Within a short period of time,
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has infected more than 823,000 people globally and resulted in over 40,000 deaths as of April 1, 2020. Swift government response to contain the outbreak requires accurate and continuous census of the infected population, particularly with regards to viral carriers without severe symptoms. We take on this task by converting
grew into a world-wide pandemic. Transmission by pre-symptomatic and asymptomatic viral carriers rendered intervention and containment of the disease extremely challenging. Based on reported infection case studies, we construct an epidemiological model that focuses on transmission around
R
time distribution, which we calibrated , into the percentage of the latent, pre-symptomatic and symptomatic groups through a novel mathematical procedure
. The model is calibrated against incubation period and serial interval statistics during early stages of the pandemic. Mathematical treatment of the model yields explicit expressions for the size of latent and pre-symptomatic subpopulations during the exponential growth phase
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estimate the
explore
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under
through
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practices
measures
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staying at home
sheltering in place
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parallel
combination
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For example, if 70\% of the general public wear masks and contact tracing is conducted at 60\% efficiency within a 4-day time frame, epidemic growth will be flattened in the hardest hit countries. Finally, we analyze the bell-shaped curves of epidemic evolution
We also compare our model behaviour to pandemic evolution data
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point out the significance of a universal decay rate of -0.32/day in the final eradication of the disease
highlight generic and less generic features of COVID-19 pandemic development