Conclusion

Interacting Population:

This metric varied wildly across our regions of interest due to massive differences in population density and available resources. The interacting population was directly shaped by the specific tier of community quarantine implemented in each area.

Transmission Rate:

This indicates how many people an infected individual can transmit the virus to per day. This rate was observed to be highest during the initial onset of the pandemic. The data strongly suggests that quick, decisive intervention protocols during a first outbreak are essential to ease overall disease burden.

Death Rate:

Serves as a way to evaluate the severity of COVID-19 and the effectiveness of interventions in reducing mortality. NCR experienced the highest death rate during the Enhanced Community Quarantine (ECQ), which implied a higher mortality risk and quarantine phases. On the other hand, Davao City maintained a relatively low death rate across quarantine phases, which suggested effective healthcare management.

Reporting Ratio:

This reflects how effectively a health system detects and responds to cases. Baguio City showcased a notably high reporting ratio during MGCQ,  indicating an effective testing strategy, strong public awareness, and adherence to health measures. Because this metric is heavily tied to the local geopolitical landscape, Local Government Units (LGUs) play a pivotal role in ensuring cases are well-accounted for.

Key Limitations:

• The model relies strictly on confirmed case and death data, meaning it lacks granular details on exposed, asymptomatic individuals and complex population interactions.

• It assumes that transmission rates and the effectiveness of control measures remain constant over time, which doesn't always reflect the chaotic nature of the real world.

• Currently, the model does not account for age structure or spatial dynamics, both of which heavily influence transmission accuracy.

Real-World Impact & Policy Making:

Despite these limitations, this modeling framework's flexibility, simplicity, and predictive power are highly valuable. In fact, it has already been integrated into a decision-support system utilized by local system developers.

For policymakers, this model serves as a virtual planning platform. It proves that leaders must tailor strategies to their specific local contexts rather than relying on a one-size-fits-all approach. Crucially, decision-makers can use these simulations to weigh the health advantages of strict lockdowns against their economic and social ramifications, ensuring vulnerable populations are protected on all fronts.

The fight against infectious diseases is constantly evolving, and so must our models.

Future studies can build upon this foundational analysis by incorporating critical modern variables, such as vaccination rates and genomic surveillance, to provide a much more comprehensive picture of the pandemic's later stages.

Furthermore, addressing the current model's limitations should be a primary focus. By integrating age structures and complex spatial dynamics into the simulation, future iterations will offer even higher accuracy and continue to serve as a vital tool for understanding and controlling the spread of viruses.