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.

Abstract. COVID-19 vaccines are rolling out in the Philippines but the supply remains limited; there is a need to optimize the distribution. In this study, we developed a COVID-19 agent-based model for Quezon City, a COVID-19 hotspot in the country. This model, in conjunction with a multi-objective linear programming model for equitable vaccine distribution, was then used to simulate four vaccination scenarios. Experiments were conducted with the front-line workers always added to the groups: mobile workers, elderly and low-income. Main results are: prioritizing the mobile workers minimizes infections the most (by 4.34%), while prioritizing the low-income groups minimizes deaths the most (by 1.93%). These results demonstrate that protecting the population with the most interactions (mobile workers) effectively reduces future infections. On the other hand, protecting the most vulnerable population (low income and elderly) decreases the likelihood of death. These results may guide the policy-makers in Quezon City.

Melanoma is considered to be the most serious and aggressive type of skin cancer, and metastasis appears to be the most important factor in its prognosis. Herein, we developed a transfer learning-based biomarker discovery model that could aid in the diagnosis and prognosis of this disease. After applying it to the ensemble machine learning model, results revealed that the genes found were consistent with those found using other methodologies previously applied to the same TCGA (The Cancer Genome Atlas) data set. Further novel biomarkers were also found. Our ensemble model achieved an AUC of 0.9861, an accuracy of 91.05, and an F1 score of 90.60 using an independent validation data set. This study was able to identify potential genes for diagnostic classification (C7 and GRIK5) and diagnostic and prognostic biomarkers (S100A7, S100A7, KRT14, KRT17, KRT6B, KRTDAP, SERPINB4, TSHR, PVRL4, WFDC5, IL20RB) in melanoma. The results show the utility of a transfer learning approach for biomarker discovery in melanoma.

UNSTRUCTURED We use the age-stratified COVID-19 infection and death distributions from China (more than 44,672 infectious as of February 11, 2020) as an estimate for a study area infection and morbidity probabilities at each age group. We then apply these probabilities into the actual age-stratified population to predict infectious individuals and deaths at peak. Testing with different countries shows the predicted infectious skewing with the country median age and age stratification, as expected. We added a Q parameter to the classic SEIR compartmental model to include the effect of quarantine (Q-SEIR). 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 Exposed and Infectious, and to flatten the curve or lower the predicted values for each compartment. These two estimates were used as a range to inform planning and response to the COVID-19 threat.

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