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Abstract
BackgroundClassic compartmental models such as the susceptible-exposed-infectious-removed (SEIR) model all have the weakness of assuming a homogenous population, where everyone has an equal chance of getting infected and dying. Since it was identified in Hubei, China, in December 2019, COVID-19 has rapidly spread around the world and been declared a pandemic. Based on data from Hubei, infection and death distributions vary with age. To control the spread of the disease, various preventive and control measures such as community quarantine and social distancing have been widely used.ObjectiveOur aim is to develop a model where age is a factor, considering the study area’s age stratification. Additionally, we want to account for the effects of quarantine on the SEIR model.MethodsWe use the age-stratified COVID-19 infection and death distributions from Hubei, China (more than 44,672 infections as of February 11, 2020) as an estimate or proxy for a study area’s infection and mortality probabilities for each age group. We then apply these probabilities to the actual age-stratified population of Quezon City, Philippines, to predict infectious individuals and deaths at peak. Testing with different countries shows the predicted number of infectious individuals skewing with the country’s median age and age stratification, as expected. We added a Q parameter to the SEIR model to include the effects of quarantine (Q-SEIR).ResultsThe projections from the age-stratified probabilities give much lower predicted incidences of infection than the Q-SEIR model. As expected, quarantine tends to delay the peaks for both the exposed and infectious groups, and to “flatten” the curve or lower the predicted values for each compartment. These two estimates were used as a range to inform the local government’s planning and response to the COVID-19 threat.ConclusionsAge stratification combined with a quarantine-modified model has good qualitative agreement with observations on infections and death rates. That younger populations will have lower death rates due to COVID-19 is a fair expectation for a disease where most fatalities are among older adults.
Highlights
The initial impression that came out of Wuhan, China, in late 2019 and early 2020 was that COVID-19 most affects older adult males with pre-existing conditions
Applying the Hubei infection probabilities on Quezon City with an age distribution as shown in Table 1 gave an estimate of 322,586 infectious individuals, which accounts for less than 10% of the population of Quezon City
Initial reports estimated the Philippines’ death or mortality rate to be at 4.70% (4.05%-5.43%) [8]. This high estimate may be explained by sampling bias, wherein severe cases may have been overrepresented because of a lack of testing. Those who are infectious but are asymptomatic or exhibit mild symptoms should be represented in the testing guidelines, as well as those who were infectious with no symptoms and have recovered
Summary
The initial impression that came out of Wuhan, China, in late 2019 and early 2020 was that COVID-19 most affects older adult males with pre-existing conditions. Even our quarantine-modified model suffered from this, and this inspired us to use age-stratified infection probabilities, which gave us a lower bound for estimates Classic compartmental models such as the susceptible-exposed-infectious-removed (SEIR) model all have the weakness of assuming a homogenous population, where everyone has an equal chance of getting infected and dying. Since it was identified in Hubei, China, in December 2019, COVID-19 has rapidly spread around the world and been declared a pandemic. Methods: We use the age-stratified COVID-19 infection and death distributions from Hubei, China (more than 44,672 infections as of February 11, 2020) as an estimate or proxy for a study area’s infection and mortality probabilities for each age group. That younger populations will have lower death rates due to COVID-19 is a fair expectation for a disease where most fatalities are among older adults
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