The extension of the Luzon-wide Enhanced Community Quarantine (ECQ) raises the question on how effective the ECQ has been to contain the spread of COVID-19. If it is, how should it be implemented after April 30 without unnecessarily paralyzing local economies over a long period of time?
It is important to tackle this question at this time because LGUs rely upon national directives for policy and decision-making. Crafting of guidelines for an extended ECQ requires foresight, one that is informed by scientific data on estimates of the effectiveness of pandemic control strategies. It requires key metrics that are primarily epidemiological in nature and must be infused with as many scientific points of view as possible. Here, we show the effect of differences in population density of communities in the analysis of the transmission of COVID-19 to help national and local officials make informed decisions whether to extend, lift, or relax community quarantine.
Is the Luzon-wide ECQ effective?
Time-series analysis shows it now takes a little longer for the number of confirmed cases to double in number. What took 3 days for the total number of cases to double now takes about 6 days to happen (Figure 1). Based on these trends, one can estimate about 9,000 to 44,000* possible cases reported by the end of April 2020. In general, this indicates the relative success of the ECQ–along with other interventions–in containing the spread of the virus. However, we must not simply rely on the number of cases as a means to project courses of actions.
Another metric that can be used to test effectiveness of the ECQ is the case fatality rate. Based on best available data as of 10 April 2020, we report an estimated case fatality rate of 5.38% and a reproduction number of 0.6398, which means that the ECQ has been effective (Figure 2). The goal is to keep bringing the reproductive number down to lower than 1 through continued medical and non-medical interventions.
Quantifying the effectiveness of the ECQ, however, is highly dependent on efforts in discovering new cases. Specifically, there are situations in which countries were able to bring their reproduction numbers down close to 1, but later testing pulled the number up, such as the case of Singapore. In the case of Korea, consistent increased testing coupled with contact tracing facilitated the detection and management of the epidemic, lowering the reproduction number of COVID-19.
Proposal on how to implement community quarantine after April 30
Successful as it may seem, an ECQ covering a wide area may not be sustainable over the long run. Prolonged restriction on the movement of goods and services over a large area (i.e. region-wide) can unnecessarily paralyze local economies. In light of this reality, our best recourse after April 30 is to implement graduated activation of ECQ depending on the level of risk in certain areas at a given time. Under this set-up, provinces (or even lower-level LGUs) may be put under ECQ depending on how close or far they are to an estimated outbreak threshold. This suggestion is made based on our analysis on the trajectory of spread and the severity of its impacts across LGUs, which varies depending on the onset of local transmission, population density, and age-group distribution.
To aid decision-making on this matter, our team continued to explore epidemiological approaches in disease mapping at the provincial level using population density as proxy measure of “outbreak spread potential” (Figure 3). By getting the ratio of the number of cases against the estimated outbreak threshold, we can determine which level of community quarantine to implement (Table 1). For instance, a province whose number of cases is at least equal to the estimated outbreak threshold should implement ECQ measures. On the other hand, a province whose number of cases is less than 75% of the estimated outbreak threshold may not declare a community quarantine at all, but only need to sustain information campaign efforts, general physical distancing, testing and contact tracing, home quarantine for probable cases, and hospitalization for patients needing care and treatment.
Data considerations to improve decision-making
The approach presented here depends entirely on the quality of official reports, testing accuracy, monitoring, and faithful accounts of fatalities, among others. There have been reports of discrepancies between the official fatality count and those reported from the ground and must be addressed to ensure quality and timeliness of data used in any analysis. Furthermore, model estimates would improve much if nationwide barangay-level COVID-19 related data are available daily. We recommend the employment of an automated LGU data collecting system. One of the possible applications to be used for this purpose has already been developed by our team and is ready for use by LGUs through the endcov.ph dashboard. By getting near real-time data, it becomes easier to project the rate of spread and identify locations of hotspots and outbreaks on a daily basis
The findings and suggestions outlined here are proposed to help the country’s efforts to curb the adverse impacts of the COVID-19 pandemic. Soon we will have to decide when to restart economic activities, and these localized metrics, which can be done up to barangay level may aid policy decisions on the preservation of both lives and livelihoods.
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