The COVID-19 pandemic exposed the vulnerabilities of global healthcare systems. The compounding effects of pandemics and natural disasters, particularly those with short return periods like wildfires, can severely strain these systems. This research addresses the lack of frameworks for quantifying the combined impact of these events on hospital networks and the absence of analytical methods for capturing dynamic spatiotemporal variations in capacity and demand. The interplay between pandemic containment measures (e.g., social distancing) and disaster response (e.g., evacuations bringing people together) creates complex challenges. The study highlights the critical need for diversified policies to manage healthcare networks under such compounding hazards. Natural disasters, such as wildfires, damage infrastructure and increase healthcare demands due to injuries and other health consequences. Epidemics, like COVID-19, directly impact well-being and strain healthcare services, but without the direct physical damage to infrastructure. Existing research often addresses natural disasters and epidemics separately, neglecting their combined effects. This study aims to fill this gap by examining the combined impact of wildfire and pandemic on a healthcare system.

Previous research has examined the independent impacts of natural disasters and epidemics on healthcare systems. Studies on natural disasters focused on household risk assessments, patient demand modeling, and community recovery. Many studies considered earthquakes and climate-related events as hazards, often focusing on single hospitals and assuming constant hospital capacity. Research on pandemics explored healthcare system challenges, increases in patient demand, and required staff-bed ratios, again frequently with the simplification of constant capacity. This study differentiates itself by directly addressing the combined impact of both a wildfire and a pandemic on a network of hospitals, a previously unexplored area.

The research uses a modified healthcare network model and incorporates data from the 2018/2019 Camp Fire in Butte County, California, simulating a simultaneous COVID-19 outbreak. A specific Interventions for Emergency Actions (IEA) model is developed to characterize infected cases based on their service needs. Disease spread parameters are estimated from various countries, accounting for uncertainties in hospitalization rates. The model dynamically distributes patient demands (wildfire and epidemic-related) to healthcare facilities using a patient-driven model. The model categorizes hospital units (ER, inpatient, ICU, ICU with ventilator) to serve different patient needs. Evacuees are considered susceptible to disease transmission in shelters, increasing acute cases and extending the disease peak. The study analyzes the effects of varying wildfire occurrence times relative to the pandemic's course on patient distribution and healthcare system performance. The impacts are assessed by analyzing changes in COVID-19 quarantined cases, patient waiting times, hospital functionality, and the number of days hospitals are overwhelmed. The analysis also incorporates several mitigation strategies, including staff reductions in treatment times, increasing physical beds, and using non-fully featured ventilators. Different disease spread rates (from various global hotspots) are considered to assess the sensitivity of results to the epidemic's severity. The study also examines the effectiveness of other mitigation strategies, such as increasing the number of shelters, providing sufficient protective equipment to medical staff, and ensuring effective staff transfer and use of non-acute care beds. Finally, linear optimization is used to determine the optimal size and location of a temporary backup hospital to minimize untreated patients and reduce waiting times. The model also incorporates data on hospital capacity, staff availability, transportation networks, and other factors.

The study reveals that the combined impact of wildfire and pandemic significantly surpasses the sum of their individual effects. The relative timing of the events greatly influences the strain on the healthcare system. When the wildfire occurs before the pandemic peak, disease spread accelerates, leading to longer waiting times and more days of overwhelmed hospitals. A wildfire during the pandemic peak has a somewhat less severe but still substantial impact. The study demonstrates a substantial increase in COVID-19 quarantined cases when wildfire and pandemic occur within a similar timeframe compared to the pandemic alone. The analysis highlights the sensitivity of patient distribution and healthcare system performance to the relative occurrence time between the two events. The model quantifies the increased demand on different hospital units (ER, inpatient, ICU, ventilators) under different wildfire-pandemic scenarios. In the worst-case scenario (wildfire occurring before the epidemic peak), the ER demand can be double the capacity, resulting in thousands of patients being sent home untreated. Inpatient demand can also significantly exceed capacity. The study evaluates various mitigation strategies, finding that implementing restrictive measures in shelters and providing adequate protective equipment for healthcare personnel can considerably improve the system's performance, reducing the number of untreated patients and the duration of hospital overload. It demonstrates that lack of effective staff replacement from damaged facilities and failure to utilize non-acute care beds substantially worsens healthcare system performance. Finally, the optimization analysis suggests the optimal location and size of a temporary backup hospital, providing crucial insights for resource allocation in such crisis situations. This temporary hospital's size and required number of beds and ventilators are highly dependent on the severity of the pandemic scenario used in the model (varying from Germany to US-level severity).

The findings highlight the crucial need for integrated preparedness plans that consider the combined effects of natural disasters and pandemics. The study underscores the importance of accounting for the dynamic interplay between these events when assessing healthcare system vulnerability and developing mitigation strategies. The model's sensitivity analysis demonstrates the need for flexible and adaptable approaches to resource allocation. The results have significant implications for emergency management, healthcare planning, and public health policy, emphasizing the critical role of coordinated efforts and the effective utilization of available resources during crises. The success of mitigation strategies relies heavily on their timely and efficient implementation. The study's findings are applicable beyond Butte County, but the specific impacts may vary based on community characteristics and preparedness levels. Future research could investigate the social and economic impacts, and the sensitivity to different community characteristics and social networks, in order to develop more robust and tailored strategies for different regions and scenarios.

This research provides a novel framework for assessing the compound impact of wildfire and epidemics on healthcare systems. The study's findings emphasize the critical need for integrated preparedness and response strategies that account for the interaction between natural disasters and pandemics. The model's ability to quantify the impact of mitigation strategies and optimize resource allocation makes it a valuable tool for healthcare planners and emergency managers. Future research could explore the model’s applicability to other types of natural disasters and epidemics, further refine the model's parameters, and investigate the social and economic consequences of compound events.

The study uses data from a specific wildfire and pandemic, and its findings may not be fully generalizable to all contexts. The model's accuracy depends on the accuracy of the input data, including disease transmission parameters and healthcare system characteristics. The model simplifies certain aspects of the healthcare system and human behavior, such as the precise dynamics of staff replacement and patient decision-making. The study assumes certain mitigation strategies are feasible and immediately implemented; their effectiveness may vary in practice. Finally, the assumptions of evacuees remaining in Butte County and unavailability of vaccines at the time could influence the results.