This calculates the “mathematical expectation” of future infections per age group, by multiplying an age group’s infection probability by the population in that age group. Initially, we used the data of patients with COVID-19 in Hubei [1], stratified by ages. As data came in, we repeated the calculations with updated Quezon City data. We treat the percentages of incidence in each age group as a proxy or estimate for the corresponding probabilities of infection for people in the corresponding age group. The true probabilities are and continue to be unknown, but the scatter of the data from Hubei is consistent with the virus affecting older adults with pre-existing conditions more than other groups (Figure 1).

Next, we took the proxy probabilities from Hubei, and applied them to the actual age stratification of the Philippines. Due to its young population, this resulted in a “skewed to the right” distribution (Figure 2) compared to the Hubei distribution, and the true distribution for the study area will be revealed as actual cases are reported.

The Philippines has a median age of 25.7 years [2] compared to China’s 38.4 years [2]. This means half of the population is aged <25.7 years, so more than half of the population will be in the “safer” age groups, with lower probabilities of getting infected, and significantly greater chances of survival if they should contract the disease. Those who do get infected account for only 10.2% of cases (see Table 1, sum of the percentages of those aged 0-29 years old). We expect this to be true for other countries with low median ages.

Using the United Nations World Population Prospects 2019 data [3], we did a similar experiment with Japan (median age 48.6 years) and Kenya (median age 20 years) [2]. We see the younger population also skewing right (Figure 3). Martinez [4] did similar calculations, but did not treat the age-stratified incidence as a proxy for infection probability.

The quarantine-modified SEIR equations are shown below.

Initial reports (around April 2020) 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 also be equally represented in the testing guidelines (at the moment, they are not), as well as those who were infectious with no symptoms and have recovered.

We tried the calculations from [4] using the death rate, which was reported at 2.3% for China [1]. This gave a much lower number of around 2857 deaths, for a Quezon City death rate of 0.89%. This figure is surprisingly low compared to the 6.94% projected using the estimated infection probability. The latest estimate of the Philippine COVID-19 death rate is 1.82% [9].

The delay in test reporting (estimate of 5-7 days [10]) factors in the estimation of the initial E-I-R values. In addition, this delay is compounded by the incubation period and, in our opinion, moves the quarantine effect further down from the actual date of implementation (March 15, 2020). We started the Q-SEIR simulation on March 20, 2020, with no quarantine assumed because the steep jump in cases occurred on this date; 60% effective quarantine was set for April 2, 2020.

The Q-SEIR model predicted 14.00% of the population will be infectious (I) at the peak. The two methods now give us a low and high estimate for Quezon City: infectious individuals will peak between 9.95% (from age stratification) and 14.00% (from Q-SEIR) of the population, around the third week of May 2020. These figures seemed high compared to the reported incidence during the same period, but were not in any way unique (compared to modeling done in other countries). At that time, the suspicion was that actual cases were undertested/underreported by as much as 90% (ie, only 10% of cases were being detected). Nevertheless, this range of values serves as a guide for planners in anticipating the need for personal protective equipment, mass testing, hospital beds, and other basic needs.

Figure 4 shows the scatter of cases for Quezon City projected by the Hubei data, and the actual scatter as of May 2020.

Like many research groups, we were and continue to be hampered by a lack of reliable data in a usable format. Even now, we refer to multiple data sets including the Philippine Department of Health’s DataDrop, WHO, and other data sources.

We were one of the first groups to forward the theory of age stratification when it comes to modeling COVID-19 infections. Recently, we came across works by Balabdaoui et al [11], Undurraga et al [12], and the WHO [13]. However, we are uniquely using incidence percentages as proxies for infection probabilities with good results.

In conclusion, age stratification predicted the scatter of cases for Quezon City fairly well. It also predicted later observations of lower cumulative confirmed cases for the same time period (eg, 6587 cases per million compared to Italy’s 58,417 cases per million as of March 28, 2021); there is a color-coded world map in [14]. Some African countries have lower median ages than the Philippines, and they have generally lighter colors than Europe and North America. The best prediction is the noticeably lower death or mortality rate of 1.82% compared to Italy’s 3.05% and Indonesia’s 2.7% (all as of March 29, 2021) [9]. Since COVID-19 disproportionately affects older adults more than younger populations, we expect the Philippines to have a lower mortality rate than countries with older populations (eg, Italy has a median age of 47.3 years and Indonesia has a median age of 29.7 years, compared to 25.7 years in the Philippines). Later work can use actual infection probabilities to include the effect of age stratification in the model.