ABSTRACT
Face à la crise sanitaire provoquée par la pandémie de Covid-19 en France, Santé publique France a mis en place un système de surveillance évolutif fondé sur des définitions de cas possible, probable et confirmé. Le décompte quotidien se limite cependant aux cas confirmés par reverse transcriptase polymerase chain reaction ou sérologie SARS-CoV-2 (actuellement via la plateforme SI-DEP), aux cas hospitalisés (via le Système d'information pour le suivi des victimes d'attentats) et aux décès hospitaliers par Covid-19. Ce suivi de la circulation virale est forcément non exhaustif, et l'estimation de l'incidence est complétée par d'autres indicateurs comme les appels au 15, les recours à SOS Médecins, les passages dans les services d'accueil des urgences, les consultations de médecine de ville via le réseau Sentinelle. Le suivi de la mortalité non hospitalière s'est heurté aux délais de transmission des certificats de décès et au manque de diagnostic fiable. Seule la létalité hospitalière a pu être mesurée de manière fiable. Moyennant un certain nombre de précautions statistiques et d'hypothèses de travail, les modèles ont permis d'anticiper l'évolution de l'épidémie à partir de deux indicateurs essentiels : le ratio de reproduction R et le temps de doublement épidémique. En Île-de-France, l'Assistance publique– Hôpitaux de Paris a complété ce tableau de bord grâce à son entrepôt de données de santé et a ainsi pu modéliser de manière fine le parcours de soins des patients. L'ensemble de ces indicateurs a été essentiel pour assurer une planification de la réponse à la crise.Alternate abstract: Facing the arrival of the COVID-19 pandemic in France, Santé Publique France has set up an evolutionary surveillance system based on definitions of possible, probable and confirmed cases. But only cases confirmed by SARSCoV-2, RT-PCR (reverse transcriptase polymerase chain reaction) or serology, hospitalized cases and in-hospital deaths have been recorded on a daily basis. COVID-19 actual incidence has thus been estimated through additional indicators such as specific calls to emergency services (Samu) and SOS doctors, emergency rooms visits, or consultations in a sentinel network of general practitioners. Surveillance of non-hospital mortality has been impaired by delays and diagnostic inaccuracies of death certificates. Only in-hospital lethality could be reliably monitored.With a few essential statistical precautions and working hypotheses, models made it possible to anticipate the evolution of the epidemic based on two essential indicators: the reproduction ratio R, and the epidemic doubling time. In Ile-de-France region, the Greater Paris University Hospitals Group has used its data warehouse to complete this epidemic dashboard, including a fine modeling of patients' care pathways. All these indicators have proved essential to plan the response to this unprecedented crisis.
ABSTRACT
As the COVID-19 pandemic has strongly disrupted people's daily work and life, a great amount of scientific research has been conducted to understand the key characteristics of this new epidemic. In this manuscript, we focus on four crucial epidemic metrics with regard to the COVID-19, namely the basic reproduction number, the incubation period, the serial interval and the epidemic doubling time. We collect relevant studies based on the COVID-19 data in China and conduct a meta-analysis to obtain pooled estimates on the four metrics. From the summary results, we conclude that the COVID-19 has stronger transmissibility than SARS, implying that stringent public health strategies are necessary.
ABSTRACT
BACKGROUND: Coronavirus disease 2019 (COVID-19) has spread rapidly in the United States since January 2020. METHODS: We estimated mean epidemic doubling time, an important measure of epidemic growth, nationally, by state, and in association with stay-at-home orders. RESULTS: The epidemic doubling time in the United States was 2.68 days (95% confidence interval [CI], 2.30-3.24 days) before widespread mitigation efforts, increasing by 460% to 15 days (12.89-17.94 days) during the mitigation phase. Among states without stay-at-home orders, the median increase in doubling time was 60% (95% CI, 9.2-223.3), compared with 269% (95% CI, 277.0-394.0) for states with stay-at-home orders. CONCLUSIONS: Statewide mitigation strategies were strongly associated with increased epidemic doubling time.