Age-Stratified Infection Probabilities Combined With a Quarantine-Modified Model for COVID-19 Needs Assessments:
Vena Pearl Bongolan1, Jose Marie Antonio Minoza1, Romulo de Castro2
1Department of Computer Science, University of the Philippines Diliman, Quezon City, Philippines.
Insights
This study models COVID-19 spread using age stratification and quarantine effects, showing age significantly impacts infection and death rates. Quarantine measures effectively flatten the curve, reducing peak infectious individuals and deaths.
Area of Science:
- Epidemiology
- Mathematical Modeling
Background:
- Classic compartmental models like SEIR assume homogenous populations, neglecting age-specific infection and mortality risks.
- COVID-19 pandemic highlighted significant variations in infection and death rates across different age groups.
Purpose of the Study:
- To develop an age-stratified SEIR model incorporating quarantine effects (Q-SEIR).
- To predict infectious individuals and deaths at peak, considering age demographics and quarantine interventions.
Main Methods:
- Utilized age-stratified COVID-19 infection and death data from Hubei, China, as probability proxies.
- Applied these probabilities to Quezon City's age-stratified population, integrating a quarantine parameter (Q) into the SEIR model.
Main Results:
- Age-stratified projections yielded lower infection incidences compared to the Q-SEIR model.
- Quarantine measures were shown to delay epidemic peaks and flatten the infection curve.
Conclusions:
- Age stratification and quarantine modifications align with observed COVID-19 infection and death patterns.
- The model supports the expectation of lower COVID-19 mortality rates in younger populations.
Background:
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. 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.
Objective:
Our 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.
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. 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).
Results:
The 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.
Conclusions:
Age 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.
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