I.M.L.)Motivated by the rapid spread of COVID-19 in Mainland China, we use a global metapopulation disease transmission model to project the impact of travel limitations on the national and international spread of the epidemic. The model is calibrated based on internationally reported cases, and shows that at the start of the travel ban from Wuhan on 23 January 2020, most Chinese cities had already received many infected travelers. The travel quarantine of Wuhan delayed the overall epidemic progression by only 3 to 5 days in Mainland China, but has a more marked effect at the international scale, where case importations were reduced by nearly 80% until mid February. Modeling results also indicate that sustained 90% travel restrictions to and from Mainland China only modestly affect the epidemic trajectory unless combined with a 50% or higher reduction of transmission in the community.

Quantifying long-range dissemination of infectious diseases is a key issue in their dynamics and control. Here, we use influenza-related mortality data to analyze the between-state progression of interpandemic influenza in the United States over the past 30 years. Outbreaks show hierarchical spatial spread evidenced by higher pairwise synchrony between more populous states. Seasons with higher influenza mortality are associated with higher disease transmission and more rapid spread than are mild ones. The regional spread of infection correlates more closely with rates of movement of people to and from their workplaces (workflows) than with geographical distance. Workflows are described in turn by a gravity model, with a rapid decay of commuting up to around 100 km and a long tail of rare longer range flow. A simple epidemiological model, based on the gravity formulation, captures the observed increase of influenza spatial synchrony with transmissibility; high transmission allows influenza to spread rapidly beyond local spatial constraints.

Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are rare but severe cutaneous adverse reactions (SCAR) related to a variety of medications. They have a significant public health impact because of high mortality and morbidity. A multinational case-control study conducted in Europe between 1997 and 2001 evaluated the risk of medications to induce SCAR. Cases were actively detected through a hospital network covering more than 100 million inhabitants. Three hospitalized patients per case matched on age, gender, and date of interview were enrolled as controls. After validation by an expert committee blinded to exposures, 379 SCAR cases and 1,505 controls were included. Among drugs recently introduced into the market, strong associations were documented for nevirapine (relative risk (RR)>22) and lamotrigine (RR>14), and weaker associations for sertraline (RR=11 [2.7-46]), pantoprazole (RR=18 [3.9-85]), and tramadol (RR=20 [4.4-93]). Strong associations were confirmed for anti-infective sulfonamides, allopurinol, carbamazapine, phenobarbital, phenytoin, and oxicam-NSAIDs , with some changes in relative numbers of exposed cases. Thus, many cases were still related to a few "old" drugs with a known high risk. Risk was restricted to the first few weeks of drug intake. The use of such drugs as first-line therapies should be considered carefully, especially when safer alternative treatments exist. A number of widely used drugs did not show any risk for SJS and TEN.

BackgroundThe coronavirus disease 2019 (COVID-19) epidemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), began in Wuhan city, Hubei province, in December, 2019, and has spread throughout China. Understanding the evolving epidemiology and transmission dynamics of the outbreak beyond Hubei would provide timely information to guide intervention policy. MethodsWe collected individual information from official public sources on laboratory-confirmed cases reported outside Hubei in mainland China for the period of Jan 19 to Feb 17, 2020. We used the date of the fourth revision of the case definition (Jan 27) to divide the epidemic into two time periods (Dec 24 to Jan 27, and Jan 28 to Feb 17) as the date of symptom onset. We estimated trends in the demographic characteristics of cases and key time-to-event intervals. We used a Bayesian approach to estimate the dynamics of the net reproduction number (R t ) at the provincial level. FindingsWe collected data on 8579 cases from 30 provinces. The median age of cases was 44 years (33-56), with an increasing proportion of cases in younger age groups and in elderly people (ie, aged >64 years) as the epidemic progressed. The mean time from symptom onset to hospital admission decreased from 4•4 days (95% CI 0•0-14•0) for the period of Dec 24 to Jan 27, to 2•6 days (0•0-9•0) for the period of Jan 28 to Feb 17. The mean incubation period for the entire period was estimated at 5•2 days (1•8-12•4) and the mean serial interval at 5•1 days (1•3-11•6). The epidemic dynamics in provinces outside Hubei were highly variable but consistently included a mixture of case importations and local transmission. We estimated that the epidemic was self-sustained for less than 3 weeks, with mean Rt reaching peaks between 1•08 (95% CI 0•74-1•54) in Shenzhen city of Guangdong province and 1•71 (1•32-2•17) in Shandong province. In all the locations for which we had sufficient data coverage of Rt, Rt was estimated to be below the epidemic threshold (ie, <1) after Jan 30. Interpretation Our estimates of the incubation period and serial interval were similar, suggesting an early peak of infectiousness, with possible transmission before the onset of symptoms. Our results also indicate that, as the epidemic progressed, infectious individuals were isolated more quickly, thus shortening the window of transmission in the community. Overall, our findings indicate that strict containment measures, movement restrictions, and increased awareness of the population might have contributed to interrupt local transmission of SARS-CoV-2 outside Hubei province.