Abstract
The conceptual framework that policymakers in Thailand can use to deal with the pandemic of the novel coronavirus SARS-CoV-2 (the COVID-19 pandemic) under the contexts of Thailand‖s health systems might be driven from compartmentalized thinking or decision supports from experts in health disciplines. But the COVID-19 pandemic is a complex problem, with interconnected causes of the health, social and economic problems, and uncertainties of how to solve such problems as only limited knowledge exists due to its nature of an emerging disease. With compartmentalized thinking, any decision-making could create unintended adverse consequences. However, epidemiologists and health systems researchers can apply concepts and tools of systems thinking to create well-rounded policy options to address the COVID-19 pandemic by investigating all interconnected relationship among factors that can influence the outcome of the disease epidemic, and considering the society as a complex adaptive system that will adapt to both the disease and the disease control policies. Literature suggests that behavioral change of stakeholders can lead to many avoidable problems of COVID-19 in Thai health systems contexts. By the nature of infectious disease, the number of infected people has not increased linearly, but non-linearly. The speed that the disease spreading out depends on the basic reproduction number (R0) that represents an average number of susceptible people who get infected from one infected person, and the effective reproduction number (Rt) that represents an average number of susceptible people who get infected from one infected person taking into the accent of the effectiveness of policies and measures in disease control, where Rt < 1 indicates an effective disease control. A causal loop diagram (CLD) was constructed from qualitative data analyses to demonstrate the relationships among all factors relevant to the epidemic. The CLD points out that many epidemic problems can be a result of adaptations of stakeholders. For instance, the number of infected people in Thailand were minimum and only gradually increased in the early phase of the epidemic, thanks to effective quarantine and isolation measures of those who came back from an international trip, as well as personal hygiene and physical distancing behaviors of the population. But the clustered epidemics at pubs, bars, and a boxing stadium in Bangkok increased the number of newly infected each day in late March led to policy-decision to use the “hammer” measures (closing workplace, stay-at-home policy, work-from-home policy, and travel bans). However, such the policies also created adverse consequences as the workers in Bangkok who lost their jobs due to the workplace close needed to travel back to their hometown, spreading the disease to all over the country. The understanding of epidemic systems from CLD led to the development of system dynamics simulation models (SD), which can be used as an integrated systems tool for addressing the COVID-19 problems by adding policies and measures upon the epidemic model, such as increasing personal hygiene and physical distancing, and enhancing the capacity of outbreak investigators and healthcare facilities. Experimenting each policy option on the simulation modeling can help policymakers decide which policy potentially results into the most effectiveness and the least unintended consequences. In the long-run, it can also be used for synthesizing healthcare reforms strategies on how to mitigate the future epidemics, reduce consequences of epidemics on the vulnerable populations and health inequity, and improve the health systems resilience and be more ready for any future crises.
