Policy Development for Pandemic Response Using System Dynamics: a Case Study on COVID-19

Uncertain SEIAR system dynamics modeling for improved community health management of respiratory virus diseases: A COVID-19 case study

The study investigates the significance of employing advanced systemic models in community health management, with a focus on COVID-19 as a respiratory virus. Through the development of a system dynamics model integrating an uncertain SEIAR model, our research addresses the critical issue of parameter uncertainty using Ensemble Kalman Filter (EnKF) and Metropolis-Hastings (MH) algorithms. We present a case study using real COVID-19 outbreaks in Iran, offering insights into effective outbreak control scenarios and considering the global impact of respiratory viruses. The research yields distinctive results, showcasing variable mortality rates (40,500 to 436,500) across scenarios in Iran. Model accuracy is rigorously evaluated using the Normalized Root-Mean-Square Deviation (NRMSD) for new cases, deaths, and recoveries (0.2 %, 1.2 %, and 0.6 % respectively). The outcomes not only contribute to the existing body of knowledge but also offer practical implications for healthcare policies, economic considerations, and sensitivity assessments related to respiratory diseases. This study stands out from others in its approach to modeling and addressing uncertainty within a system dynamics framework. The integration of EnKF and MH algorithms provides a nuanced understanding of parameter uncertainty, adding a layer of sophistication to the analysis. The application of the model to real-world COVID-19 outbreaks in Iran further enhances the study's relevance and applicability. In conclusion, the research introduces an uncertain SEIAR system dynamics model with unique contributions to policymaking, economic considerations, and sensitivity assessments for respiratory diseases. The outcomes and insights derived from the study not only advance our understanding of disease dynamics but also provide actionable information for effective public health management.

Telehealth program for symptomatic COVID-19 patients in Mindanao, Philippines: a whole-of-system, pragmatic interventional study on patient monitoring from isolation facilities to community reintegration

The COVID-19 pandemic is impacting individuals and society's physical and mental health. Despite the lack of any definite and effective therapeutic regimen, public health measures such as quarantine and isolation have been instituted to contain this pandemic. However, these mitigating measures have also raised issues regarding isolated patients' mental and psychological well-being. Several stakeholders were engaged in this approach, including the university, the local health office, the tertiary hospital, and the local communities. This intervention addresses concerns regarding the health status of isolated individuals due to COVID-19 infection, making the program available to anyone who agrees to participate. This was done through telehealth services delivered via phone calls and SMS. The university provided technical support and telehealth manpower through medical students. The local health unit manages the isolation facilities, while the referral hospital offers specialty care for isolated patients through teleconsultation. Finally, the local community is the one that reintegrates discharged patients into their communities. Three hundred forty-four (344) participants were provided seven sessions on telehealth education and tracking of their COVID-19 prescribed practices and mental health. The mean age of the patients was 37 years; half were females, and 15% had comorbidities. Regarding their mental health status, the level of depression dropped from 6% to 1% (p<0.0001), the level of anxiety dropped from 12% to 2% (p<0.0001), and the level of stress dropped from 3% to 0% (p<0.0001) from the first day of admission to 2 weeks after discharge. Moreover, a general trend of statistically significant increase in various practices was noted: wearing face masks, physical distancing, disinfecting frequently held objects, hand hygiene, and self-monitoring for COVID-19 symptoms. Those with progressing symptoms of COVID-19 were referred immediately to the referral hospital. There were also no reports of complications of co-morbidities during their stay in the isolation facilities or social isolation upon community reintegration. The study concludes that telehealth services have the potential to address many challenges in providing continuous healthcare services to isolated patients until they are reintegrated into their community. Furthermore, a whole-of-society approach is necessary to provide holistic care to patients affected by the pandemic.

Planning for healthcare services during the COVID-19 pandemic in the Southeast of England: a system dynamics modelling approach

OBJECTIVES

To develop, test, validate and implement a system dynamics model to simulate the pandemic progress and the impact of various interventions on viral spread, healthcare utilisation and demand in secondary care.

DESIGN

We adopted the system dynamics model incorporating susceptible, exposed, infection and recovery framework to simulate the progress of the pandemic and how the interventions for the COVID-19 response influence the outcomes with a focus on secondary care.

SETTING

This study was carried out covering all the local health systems in Southeast of England with a catchment population of six million with a specific focus on Kent and Medway system.

PARTICIPANTS

Six local health systems in Southeast of England using Kent and Medway as a case study.

INTERVENTIONS

Short to medium 'what if' scenarios incorporating human behaviour, non-pharmaceutical interventions and medical interventions were tested using the model with regular and continuous feedback of the model results to the local health system leaders for monitoring, planning and rapid response as needed.

MAIN OUTCOME MEASURES

Daily output from the model which included number infected in the population, hospital admissions needing COVID-19 care, occupied general beds, continuous positive airway pressure beds, intensive care beds, hospital discharge pathways and deaths.

RESULTS

We successfully implemented a regional series of models based on the local population needs which were used in healthcare planning as part of the pandemic response.

CONCLUSIONS

In this study, we have demonstrated the utility of system dynamics modelling incorporating local intelligence and collaborative working during the pandemic to respond rapidly and take decisions to protect the population. This led to strengthened cooperation among partners and ensured that the local population healthcare needs were met.

Strategic planning for the optimal distribution of COVID-19 vaccines

This work presents a multi-objective optimization strategy for fair vaccine allocation through different fairness schemes. The proposed approach considers a diverse series of parameters related to different public health data and social behaviors that influence the correct distribution of vaccines, such as corruption and crime. Simultaneously, the formulation includes prioritizing those groups with the highest risk based on the epidemiological traffic light. Furthermore, the presented strategy involves different budget constraints that allow identifying trade-off solutions through Pareto fronts. Therefore, vaccine allocations are obtained by combining fairness concepts with multi-objective optimization. The applicability of the model is illustrated using the case study of Mexico. The solution to the proposed scenarios was carried out using different justice schemes and an economic objective function. The results show the compromises between a satisfaction index and costs, which are shown through Pareto optimal solutions that allow selecting the solutions that balance the objectives. The solutions provided by the social welfare scheme suggest a greater allocation of vaccines to those states with higher epidemiological risk, which may be helpful in the first stage of vaccination. On the other hand, the Rawlsian scheme provides more balanced solutions that can be useful in situations with lower rates of infection. Finally, the Nash scheme is the one that provides the most balanced solutions, favoring to a lesser extent the areas with the highest epidemiological risk, which may be useful in the later stages of vaccination.

Modeling the resilience of urban mobility when exposed to the COVID-19 pandemic: A qualitative system dynamics approach

In December 2019, coronavirus disease (COVID-19) was detected in Wuhan, China. Due to the rapid spread of the disease, containment measures were adopted, which caused unprecedent shifts in individual mobility. Although some studies explored the impacts of the COVID-19 pandemic on travel patterns and resilience of transport systems based on different analysis techniques, there is a lack of studies addressing the impacts of the pandemic on the sustainability and resilience of urban mobility systems using in-depth and holistic methods, such as system dynamics. This research aims to characterize the dynamics present in urban mobility systems when exposed to pandemics and analyze the changes needed for systems to increase their resilience to pandemics using qualitative system dynamics modeling. The framework comprises the characterization of cause-and-effect relationships and the creation of systems' causal loop diagrams (CLD) in their basic state of functionality, when affected by pandemics, and still operating owing to its resilience. Our findings indicated that the CLD of a resilient system is driven by strategic preparedness and response plans, as well as research and development, which balance the spread of the pandemic and increase support on technological strengths and the activities performed from home.

A simulation approach for COVID-19 pandemic assessment based on vaccine logistics, SARS-CoV-2 variants, and spread rate

Despite advances in clinical care for the coronavirus (COVID-19) pandemic, population-wide interventions are vital to effectively manage the pandemic due to its rapid spread and the emergence of different variants. One of the most important interventions to control the spread of the disease is vaccination. In this study, an extended Susceptible-Infected Healed (SIR) model based on System Dynamics was designed, considering the factors affecting the rate of spread of the COVID-19 pandemic. The model predicts how long it will take to reach 70% herd immunity based on the number of vaccines administered. The designed simulation model is modeled in AnyLogic 8.7.2 program. The model was performed for three different vaccine supply scenarios and for Turkey with ~83 million population. The results show that, with a monthly supply of 15 million vaccines, social immunity reached the target value of 70% in 161 days, while this number was 117 days for 30 million vaccines and 98 days for 40 million vaccines.

The impact of inspection policies on reducing disease prevalence in public buildings: A systems dynamics approach

The occurrence of the COVID-19 pandemic revealed new dimensions of urban resilience to communities. Failure to implement health protocols in public buildings has had a significant impact on the spread of the disease, and inspection has become necessary to enforce the rules. This study presents different inspection policies of public buildings to reduce disease prevalence. It evaluates and compares the implementation of these policies in the long run based on the systems dynamics approach. First, baseline modeling was performed without inspection to analyze the proposed policies, and disease prevalence was investigated. Then various proposed inspection and fines policies, including fixed inspection and fines rate (FIFF), fixed inspection rate with the variable fine rate (FIVF), and variable inspection and fines rate (VIVF), are introduced, and their system dynamics models are presented. The impact of each inspection policy on the violations rate and disease prevalence in public buildings has been investigated using long-term simulation. Based on the results, regulatory agencies can significantly reduce the rate of violations in public buildings and improve urban resilience to the epidemic by adopting proper inspection policies. The results can help city managers to adopt appropriate inspection policies.

Using simulation modelling and systems science to help contain COVID-19: A systematic review

This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent-based model (ABM) and discrete event simulation (DES), and their hybrids in COVID-19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID-19 transmission dynamics, 204 evaluated both pharmaceutical and non-pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID-19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID-19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio-economic systems involved.

System dynamics analysis on the effectiveness of vaccination and social mobilization policies for COVID-19 in the United States

The COVID-19 pandemic has presented significant public health and economic challenges worldwide. Various health and non-pharmaceutical policies have been adopted by different countries to control the spread of the virus. To shed light on the impact of vaccination and social mobilization policies during this wide-ranging crisis, this paper applies a system dynamics analysis on the effectiveness of these two types of policies on pandemic containment and the economy in the United States. Based on the simulation of different policy scenarios, the findings are expected to help decisions and mitigation efforts throughout this pandemic and beyond.

Modeling COVID-19 effects on SDGs using system dynamics in Egypt

The coronavirus disease 2019 (COVID-19) poses a significant threat to achieving the Sustainable Development Goals (SDGs). To address this challenge, a thorough examination of the pandemic's influence on four SDGs in Egypt is presented in a system dynamic model. The addressed goals are related to no poverty (SDG 1), zero hunger (SDG 2), decent work and economic growth (SDG 8), and climate action (SDG 13). The model is simulated over 35 years extending from 2015 to 2050. Furthermore, a web-based interactive learning environment is developed to analyze the interdependencies among public health activities and study the impacts of possible intervention countermeasures or prevention policies. Indicators including poverty line, food insecurity, gross domestic product (GDP) growth rate, and greenhouse gas (GHG) emissions are evaluated to track Egypt's performance in relation to SDGs 1, 2, 8, and 13. According to the simulation model, the poverty line will continue to decline until it reaches around 16% by 2050. According to the significant governmental efforts to follow its vision of 2030, Egypt can achieve a decreasing percentage of food insecurity, reaching 3% in 2030, and this percentage will continue to decrease until it reaches full sufficiency by 2050. The GDP growth rate will rise every year until it reaches 13.71% in 2050. With respect to climate, GHG emissions are predicted to fall to roughly 97 Mt CO2-equivalents by 2050. This approach revitalizes debates about the achievement of SDGs amid the crisis and acts as a powerful tool that aids decision-makers in identifying leverage points to avoid the long-term negative repercussions of the crisis on the economy, people, and environment.

Fair Allocation of Potential COVID-19 Vaccines Using an Optimization-Based Strategy

The fair allocation of resources among multiple stakeholders in any area is a complex challenge for decision-making. This paper presents an optimization strategy for the allocation of COVID-19 vaccines, when they are available, through different fairness schemes (social welfare, Nash, Rawlsian justice, and social welfare II scheme). The applicability of the proposed model is illustrated using the case study of Mexico, including the states of the country as stakeholders. We involve several parameters to guide the allocation, such as the size, risk profiles, and fraction of vulnerable groups in the population. Furthermore, different scenarios of the availability of potential COVID-19 vaccines were evaluated. The social welfare approach is the most commonly used scheme for the allocation of resources. However, we demonstrate that this scheme yields non-unique or multiple solutions (through the social welfare II approach). These social welfare approaches provide inequalities in the allocations that become critical when resources are scarce. Specifically, the social welfare scheme favors large stakeholders (greater population) in all scenarios. We also observe how the complexity of the allocation increases with the higher availability of vaccines. Hence, it is relevant to consider allocation schemes to identify fair solutions.

Systems Dynamics Modeling of Pandemic Influenza for Strategic Policy Development: a Simulation-Based Analysis of the COVID-19 Case

The novel coronavirus disease 2019 (COVID-19) is a truly wicked problem which has remained a stubborn issue plaguing multiple countries worldwide. The continuously increasing number of infections and deaths has driven several countries to implement control and response strategies including community lockdowns, physical distancing, and travel bans with different levels of success. However, a disease outbreak and the corresponding policies can cause disastrous economic consequences due to business closures and risk minimization behaviors. This paper develops a system dynamics framework of a disease outbreak system covering various policies to evaluate their effectiveness in mitigating transmission and the resulting economic burden. The system dynamics modeling approach captures the relationships, feedbacks, and delays in such a system, revealing meaningful insights on the dynamics of several response strategies.