The pandemic of Coronavirus Disease 2019 (Covid-19) comprises complex health and socio-economic issues. The limited knowledge of this emerging disease has led to uncertainties in solving the problem. Policymakers need comprehensive data to support policy decisions and reduce the constraints of compartmentalized thinking. The literature review suggests that controlling the pandemic depends on developing policy decision processes that effectively deal with public health emergencies at the national policy level. The governance structure of the "Information and Strategy Department" is required to collect data and present it to the "Incidence Commander", who makes the decisions, develops guidelines, and communicates risks to the public. Systems thinking can be applied to the policy process by better understanding the relationships between all connected system components from the perspective of all relevant stakeholders and by testing the effectiveness of policy scenarios by simulation modeling. Thus, the process enables policymakers to make well-informed decisions supported by sound scientific evidence, likely to create optimal disease control with negative impacts and manage the risks that may arise from those decisions. A vital tool to synthesize evidence is the mathematical models that can predict epidemiological scenarios by considering the natural transmission capacity of the 2019 coronavirus. Yet they are still too limited for supporting more comprehensive policy decision-making, as the COVID-19 pandemic comprises complex health and socio-economic issues. The Susceptible-Exposed-Infectious-Recovered (SEIR) model customarily used for epidemiological forecasting can be modified to build a structure of system dynamics (SD) models that include all components of health systems resilience. So the SD model can function as an analytical tool that considers health systems' short-term absorptive, immediate-term adaptative, and long-term transformative capacities. We applied the group model buildings (GMB) process to work more closely with stakeholders to integrate all aspects of problems and solutions. Disease control measures, enhancing healthcare capacity measures, and remedial measures for economic and social consequences were considered in our modeling. Then we can use the simulation model to test policy options and effectively support the policy decision-making process in this public health emergency. Both qualitative and quantitative data were collected from participants in our workshop on health systems design in the COVID-19 era to synthesize a causal loop diagram that demonstrates the relationships of all factors within the health system affecting the control of the COVID-19 pandemic in Thailand. Additional quantitative data were collected continuously between 2020 and 2021 to construct the model for testing policy options and support the policy decision making process in several key policy options, including 1) lifting the intensive disease control measures for remedying socio-economic consequences, including reducing the quarantine duration of inbound international travelers, after the 1st wave of the COVID-19 pandemic in Thailand (the January 2020 outbreak), 2) implementing "lockdown" measures in hyperepidemic areas in the 3rd wave of the COVID-19 pandemic in Thailand (the April 2021 outbreak) at both the national level and in the Greater Bangkok Area, 3) raising the capacity of testing, contact tracing, and isolation and quarantine systems after the enforcement of lockdown measures in July 2021, and 4) enhancing the healthcare capacity to sufficiently care for COVID-19 patients during the April 2021 and the January 2022 outbreaks. The findings from our simulation model show that new waves of outbreaks can reemerge after being well controlled in early 2020. The so-called "lockdown policies," including the closure of government offices and public services and banning cross-provincial and international travel, had been implemented since March 2020. The need for easing intensive disease control measures arises due to the far-reaching socio-economic negative consequences of the government authorizing intensive disease control measures to cope with the first wave of the COVID-19 epidemic in Thailand. Therefore, policymakers should consider adopting integrated policies aimed at disease control and remedying its socio-economic impacts. For instance, subsidizing businesses affected by the lockdown policies to maintain local employment can reduce workers' traveling across territories and lower the risk of spreading the COVID-19 nationwide. Our model also suggested that at both the national level and in the Greater Bangkok Areas during the April 2021 wave, a more severe outbreak than that of January 2020 may happen due to the rapid transmissions capability of Alpha and Delta strains. The number of COVID-19 infections likely exceeded the capacity of the health service systems in some areas, especially in the Greater Bangkok Areas. Yet the effectiveness of disease control can improve if intensive lockdown policies can reduce at least 40% of community transmissions in approximately month of lockdown. Hence, policymakers should ensure that the lockdown policies are effectively enforced intensively enough in the short term. Moreover, the previous policy of isolating all infected people in hospitals should be adjusted by providing additional community isolation services, especially for those with no or minor symptoms, to cope with the limited existing healthcare capacity. The simulation also shows the outcomes of enhancing the performance of testing, contract tracing, and quarantine and isolation systems during the April 2021 outbreak. Proactive screenings in the communities could increase the reported cases in the short run but reduce the number of patients in the long run. Therefore, policymakers should consider implementing a more proactive community screening during the enforcement of lockdown measures in July 2021 to provide more access to testing and take advantage of the opportunity that vulnerable populations still have limited travel during the lockdown. The rapid lateral flow tests, or antigen test kit (ATK), should also be incorporated for screenings, even though it may lead to some false-negative results. As many people still have limited access to real-time polymerase chain reaction (RT-PCR) testing, ATK can help control the disease than using RT-PCR alone for testing. Lastly, the models show the outcomes of optimizing health care systems to provide adequate care for COVID-19 patients during the April 2021 and the January 2022 outbreaks. The number of available hospitals beds may not be sufficient to support the April 2021 outbreak, especially after the outbreak of the Delta strain in June 2021. Hence, it was necessary to build a surge capacity of health care delivery systems, such as creating additional quarantine and isolation services in hotels or the communities. The capacity of healthcare delivery systems for the January 2022 outbreak was likely insufficient to be used as the isolation facilities for an even larger number of people infected with the Omicron strain, which is even more contagious than the Delta. The simulation also shows the most critical policy that could prevent an overrun of healthcare delivery systems is accelerating the vaccination coverage of vulnerable groups to reduce the morbidity and case-fatality mortality rate. The preliminary results of our research have been presented to policymakers at different levels to support their decision-making process. They included the scientific advisory committees of The Centre For COVID-19 Situation Administration (CCSA) of the Royal Thai Government, The Emergency Operation Center (EOC) of The Ministry of Public Health, The Macroeconomics Department of the Central Bank of Thailand, and The Board of Directors of the Health Systems Research Institute. The study findings were also communicated to the public through the researchers' website and the mass media periodically. Nonetheless, using system dynamics modeling as a policy decision support tool (DST) considering health systems resilience still has limitations. The public policy process to cope with Thailand's public health emergencies tends to focus more on reactive measures than on planning and addressing the structural issues of the health systems that created persisting problems during the COIVD-19 pandemic. Without policies that address the high-leverage point of the systems, many problems have persisted more than two years after the pandemic began. Therefore, it is crucial to create a learning platform among policymakers and stakeholders to synthesize lessons learned on integrating research in the public policy process and what governance process can be further improved in public health emergencies. By focusing on the evidence-informed policy-making process under the country's emergency management systems, Thailand's health system can be more fully developed as a "learning health system," an important concepts suggested by the World Health Organization. It can be challenging for policymakers to use evidence from simulation modeling in the decision-making process amid uncertain information and pressure from the public. Nonetheless, it is vital to work on this complex problem-solving process to prepare for any soon emerging health, economic, and social problems.