Predicting and forecasting the impact of local outbreaks of COVID-19: use of SEIR-D quantitative epidemiological modelling for healthcare demand and capacity.
Predicting and forecasting the impact of local outbreaks of COVID-19: use of SEIR-D quantitative epidemiological modelling for healthcare demand and capacity.
复制标题
DOI：
10.1093/ije/dyab106
复制
发表时间：
2021-08-30
影响因子：
7.7
通讯作者：
Madzvamuse A
中科院分区：
文献类型：
--
作者：
Campillo-Funollet E;Van Yperen J;Allman P;Bell M;Beresford W;Clay J;Dorey M;Evans G;Gilchrist K;Memon A;Pannu G;Walkley R;Watson M;Madzvamuse A
世界正在经历导致COVID-19疾病的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。旨在建立适用的流行病学模型，准确预测和预测当地疫情的影响，指导当地的医疗需求和能力、政策制定和公共卫生决策。该模型利用了英格兰东南部苏塞克斯郡（人口170万）当地国家卫生服务（NHS）医院的每日COVID-19情况汇总报告（包括每日入院、出院和床位占用数）以及与COVID-19相关的每周医院和其他场所死亡人数。这些数据集与2020年3月24日至6月15日的第一波COVID-19感染相对应。基于当地/区域监测数据，建立了一种新的流行病学预测和预测模型。通过严格的逆参数推理方法，将模型拟合到数据的最优意义上，估计模型参数，然后进行验证。推断的参数物理合理，并与Biggerstaff M， Cowling BJ, cucunub<e:1> ZM等人从国家数据集中获得的广泛使用的参数值相匹配。（从COVID-19关键流行病学参数的统计和数学建模的早期见解，新出现的传染病。）2020; 26(11))。我们通过使用可用数据的一个子集来验证模型的预测能力，并比较模型对未来10天、20天和30天的预测。该模型在预测中显示出很高的准确性，即使仅使用20个数据点进行拟合。我们已经证明，通过使用本地/区域数据，我们的预测和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/浪潮可能如何影响区域人口，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早期预警系统相结合，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。模型算法已被整合到一个基于网络的多机构工具包中，可供英国其他地区和其他地方的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制定的，是对COVID-19传播的不同情景和影响进行预测和预测的基础。
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. 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. 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. 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.
登录
查看更多内容
影响因子：
4.4
作者：
Boileau, Thierry;Leboeuf, Nicolas;Meibody-Tabar, Farid
通讯作者：
Meibody-Tabar, Farid
SARS-COV-2血统的估计可传播和影响B.1.1.7在英国。
DOI：
10.1126/science.abg3055
发表时间：
2021-04-09
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Davies NG;Abbott S;Barnard RC;Jarvis CI;Kucharski AJ;Munday JD;Pearson CAB;Russell TW;Tully DC;Washburne AD;Wenseleers T;Gimma A;Waites W;Wong KLM;van Zandvoort K;Silverman JD;CMMID COVID-19 Working Group;COVID-19 Genomics UK (COG-UK) Consortium;Diaz-Ordaz K;Keogh R;Eggo RM;Funk S;Jit M;Atkins KE;Edmunds WJ
通讯作者：
Edmunds WJ
影响因子：
19
作者：
Ganyani, Tapiwa;Kremer, Cecile;Hens, Niel
通讯作者：
Hens, Niel
影响因子：
56.9
作者：
Kissler, Stephen M.;Tedijanto, Christine;Lipsitch, Marc
通讯作者：
Lipsitch, Marc
影响因子：
3.7
作者：
Petropoulos, Fotios;Makridakis, Spyros
通讯作者：
Makridakis, Spyros