Background The world is experiencing local/regional hotspots and spikes in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19 disease. We aimed to formulate an applicable epidemiological model to accurately predict and forecast the impact of local outbreaks of COVID-19 to guide the local healthcare demand and capacity, policy-making and public health decisions. Methods The model utilized the aggregated daily COVID-19 situation reports (including counts of daily admissions, discharges and bed occupancy) from the local National Health Service (NHS) hospitals and COVID-19-related weekly deaths in hospitals and other settings in Sussex (population 1.7 million), Southeast England. These data sets corresponded to the first wave of COVID-19 infections from 24 March to 15 June 2020. A novel epidemiological predictive and forecasting model was then derived based on the local/regional surveillance data. Through a rigorous inverse parameter inference approach, the model parameters were estimated by fitting the model to the data in an optimal sense and then subsequent validation. Results The inferred parameters were physically reasonable and matched up to the widely used parameter values derived from the national data sets by Biggerstaff M, Cowling BJ, Cucunubá ZM et al. (Early insights from statistical and mathematical modeling of key epidemiologic parameters of COVID-19, Emerging infectious diseases. 2020;26(11)). We validate the predictive power of our model by using a subset of the available data and comparing the model predictions for the next 10, 20 and 30 days. The model exhibits a high accuracy in the prediction, even when using only as few as 20 data points for the fitting. Conclusions We have demonstrated that by using local/regional data, our predictive and forecasting model can be utilized to guide the local healthcare demand and capacity, policy-making and public health decisions to mitigate the impact of COVID-19 on the local population. Understanding how future COVID-19 spikes/waves could possibly affect the regional populations empowers us to ensure the timely commissioning and organization of services. The flexibility of timings in the model, in combination with other early-warning systems, produces a time frame for these services to prepare and isolate capacity for likely and potential demand within regional hospitals. The model also allows local authorities to plan potential mortuary capacity and understand the burden on crematoria and burial services. The model algorithms have been integrated into a web-based multi-institutional toolkit, which can be used by NHS hospitals, local authorities and public health departments in other regions of the UK and elsewhere. The parameters, which are locally informed, form the basis of predicting and forecasting exercises accounting for different scenarios and impacts of COVID-19 transmission.

中文翻译：

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预测和预测当地 COVID-19 疫情爆发的影响：使用 SEIR-D 定量流行病学模型来评估医疗保健需求和能力

背景 世界各地正在经历严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 的局部/区域热点和高峰，该病毒会导致 COVID-19 疾病。我们的目标是制定一个适用的流行病学模型来准确预测和预测当地爆发的COVID-19疫情的影响，以指导当地的医疗需求和能力、政策制定和公共卫生决策。方法 该模型利用了苏塞克斯当地国家卫生服务 (NHS) 医院每日汇总的 COVID-19 情况报告（包括每日入院、出院和床位占用情况）以及苏塞克斯医院和其他机构中与 COVID-19 相关的每周死亡人数（人口 170 万），英格兰东南部。这些数据集对应于2020年3月24日至6月15日的第一波COVID-19感染。然后根据当地/区域监测数据得出了一种新的流行病学预测模型。通过严格的逆参数推理方法，通过将模型与数据进行最佳拟合来估计模型参数，然后进行后续验证。结果 推断的参数在物理上是合理的，并且与 Biggerstaff M、Cowling BJ、Cucunubá ZM 等人从国家数据集中导出的广泛使用的参数值相匹配。（对 COVID-19 关键流行病学参数的统计和数学模型的早期见解，新发传染病。2020；26(11)）。我们通过使用可用数据的子集并比较未来 10、20 和 30 天的模型预测来验证模型的预测能力。即使仅使用少至 20 个数据点进行拟合，该模型也能表现出较高的预测精度。结论 我们已经证明，通过使用当地/区域数据，我们的预测模型可用于指导当地医疗保健需求和能力、政策制定和公共卫生决策，以减轻 COVID-19 对当地人口的影响。了解未来的 COVID-19 高峰/浪潮可能如何影响该地区人口，使我们能够确保及时调试和组织服务。该模型时间安排的灵活性与其他预警系统相结合，为这些服务制定了一个时间框架，以准备和隔离区域医院内可能和潜在需求的能力。该模型还允许地方当局规划潜在的太平间容量并了解火葬场和埋葬服务的负担。该模型算法已集成到基于网络的多机构工具包中，可供英国其他地区和其他地区的 NHS 医院、地方当局和公共卫生部门使用。这些参数是根据当地情况提供的，构成了预测和预报演习的基础，可考虑到 COVID-19 传播的不同情况和影响。