On 11 March 2020, the World Health Organization (WHO) declared coronavirus disease 2019 (COVID-19) a pandemic 1. The strategies based on non-pharmaceutical interventions that were used to contain the outbreak in China appear to be effective 2 , but quantitative research is still needed to assess the efficacy of non-pharmaceutical interventions and their timings 3. Here, using epidemiological data on COVID-19 and anonymized data on human movement 4,5 , we develop a modelling framework that uses daily travel networks to simulate different outbreak and intervention scenarios across China. We estimate that there were a total of 114,325 cases of COVID-19 (interquartile range 76,776-164,576) in mainland China as of 29 February 2020. Without non-pharmaceutical interventions, we predict that the number of cases would have been 67-fold higher (interquartile range 44-94-fold) by 29 February 2020, and we find that the effectiveness of different interventions varied. We estimate that early detection and isolation of cases prevented more infections than did travel restrictions and contact reductions, but that a combination of non-pharmaceutical interventions achieved the strongest and most rapid effect. According to our model, the lifting of travel restrictions from 17 February 2020 does not lead to an increase in cases across China if social distancing interventions can be maintained, even at a limited level of an on average 25% reduction in contact between individuals that continues until late April. These findings improve our understanding of the effects of non-pharmaceutical interventions on COVID-19, and will inform response efforts across the world.

The global population at risk from mosquito-borne diseases—including dengue, yellow fever, chikungunya and Zika—is expanding in concert with changes in the distribution of two key vectors: Aedes aegypti and Aedes albopictus . The distribution of these species is largely driven by both human movement and the presence of suitable climate. Using statistical mapping techniques, we show that human movement patterns explain the spread of both species in Europe and the United States following their introduction. We find that the spread of Ae. aegypti is characterized by long distance importations, while Ae. albopictus has expanded more along the fringes of its distribution. We describe these processes and predict the future distributions of both species in response to accelerating urbanization, connectivity and climate change. Global surveillance and control efforts that aim to mitigate the spread of chikungunya, dengue, yellow fever and Zika viruses must consider the so far unabated spread of these mosquitos. Our maps and predictions offer an opportunity to strategically target surveillance and control programmes and thereby augment efforts to reduce arbovirus burden in human populations globally.

BackgroundThe COVID-19 outbreak containment strategies in China based on non-pharmaceutical interventions (NPIs) appear to be effective. Quantitative research is still needed however to assess the efficacy of different candidate NPIs and their timings to guide ongoing and future responses to epidemics of this emerging disease across the World. MethodsWe built a travel network-based susceptible-exposed-infectious-removed (SEIR) model to simulate the outbreak across cities in mainland China. We used epidemiological parameters estimated for the early stage of outbreak in Wuhan to parameterise the transmission before NPIs were implemented. To quantify the relative effect of various NPIs, daily changes of delay from illness onset to the first reported case in each county were used as a proxy for the improvement of case identification and isolation across the outbreak. Historical and near-real time human movement data, obtained from Baidu location-based service, were used to derive the intensity of travel restrictions and contact reductions across China. The model and outputs were validated using daily reported case numbers, with a series of sensitivity analyses conducted. FindingsWe estimated that there were a total of 114,325 COVID-19 cases (interquartile range [IQR] 76,776 -164,576) in mainland China as of February 29, 2020, and these were highly correlated (p<0.001, R 2 =0.86) with reported incidence.Without NPIs, the number of COVID-19 cases would likely have shown a 67-fold increase (IQR: 44 -94), with the effectiveness of different interventions varying. The early detection and isolation of cases was estimated to prevent more infections than travel restrictions and contact reductions, but integrated NPIs would achieve the strongest and most rapid effect. If NPIs could have been conducted one week, two weeks, or three weeks earlier in China, cases could have been reduced by 66%, 86%, and 95%, respectively, together with significantly reducing the number of affected areas. However, if NPIs were conducted one week, two weeks, or three weeks later, the number of cases . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.is the (which was not peer-reviewed) The copyright holder for this preprint .

SUMMARY Background Avian influenza A(H7N9) virus has caused human infections in China since 2013, and a large epidemic in 2016–17 has prompted concerns of whether the epidemiology has changed to suggest an increasing pandemic threat. Our study aimed to describe the epidemiological characteristics, clinical severity, and time-to-event distributions of A(H7N9) case-patients in the 2016–17 epidemic wave compared with previous waves. Methods We obtained information about all laboratory-confirmed human cases of A(H7N9) virus infection reported in mainland China as of 23 February 2017. We described the epidemiological characteristics across epidemic waves, and estimated the risk for death, mechanical ventilation, and admission to the intensive care unit for patients who required hospitalization for medical reasons. We estimated the incubation periods, and time delays from illness onset to hospital admission, illness onset to initiation of antiviral treatment, and hospital admission to death or discharge. Findings The 2016–17 A(H7N9) epidemic wave began earlier, spread to more counties in affected provinces and had more confirmed cases than previous epidemic waves. There was an increase in the proportion of cases in middle-aged adults and in semi-urban and rural residents. The clinical severity of hospitalized cases in 2016–17 was comparable to the previous epidemic waves. Interpretation Age distribution and case sources changed gradually across epidemic waves, while clinical severity has not changed substantially. Continued vigilance and sustained intensive control efforts are needed to minimize the risk of human infection with A(H7N9) virus. Funding The National Science Fund for Distinguished Young Scholars (grant no. 81525023).

BackgroundDengue has been a notifiable disease in China since 1 September 1989. Cases have been reported each year during the past 25 years of dramatic socio-economic changes in China, and reached a historical high in 2014. This study describes the changing epidemiology of dengue in China during this period, to identify high-risk areas and seasons and to inform dengue prevention and control activities.MethodsWe describe the incidence and distribution of dengue in mainland China using notifiable surveillance data from 1990-2014, which includes classification of imported and indigenous cases from 2005-2014.ResultsFrom 1990-2014, 69,321 cases of dengue including 11 deaths were reported in mainland China, equating to 2.2 cases per one million residents. The highest number was recorded in 2014 (47,056 cases). The number of provinces affected has increased, from a median of three provinces per year (range: 1 to 5 provinces) during 1990-2000 to a median of 14.5 provinces per year (range: 5 to 26 provinces) during 2001-2014. During 2005-2014, imported cases were reported almost every month and 28 provinces (90.3%) were affected. However, 99.8% of indigenous cases occurred between July and November. The regions reporting indigenous cases have expanded from the coastal provinces of southern China and provinces adjacent to Southeast Asia to the central part of China. Dengue virus serotypes 1, 2, 3, and 4 were all detected from 2009-2014.ConclusionsIn China, the area affected by dengue has expanded since 2000 and the incidence has increased steadily since 2012, for both imported and indigenous dengue. Surveillance and control strategies should be adjusted to account for these changes, and further research should explore the drivers of these trends.Please see related article: http://dx.doi.org/10.1186/s12916-015-0345-0Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-015-0336-1) contains supplementary material, which is available to authorized users.

The emergence of SARS-CoV-2 SARS-CoV-2 related coronavirusesMany of the early cases of COVID-19 in Wuhan, China, were associated with the Huanan Seafood Market 2 , which-because of the presence of wildlife at the market-was considered an obvious candidate for the location of the initial zoonotic (that is, cross-species transmission) event. However, none of the animals from the market (including rabbits, snakes, stray cats, badgers and bamboo rats) tested positive for SARS-CoV-2 (ref. 7 ), and viral genome sequences of environmental samples from the market were not considered to occupy basal positions on the viral phylogeny (although the position of the rooting on the tree is uncertain) 8 . In addition, some of the early cases of COVID-19 in Wuhan were not epidemiologically linked to the market 9 , and some were linked to other markets 10,11 . Therefore, although it has not been resolved fully, the current evidence suggests that the Huanan Seafood Market could be the location of an early 'superspreading' event.From the earliest genomic comparisons, it was clear that SARS-CoV-2 had a genomic organization similar to SARS-CoV 2 . The spike proteins of both viruses have similar three-dimensional structures, suggesting that these viruses might use the same cell surface receptor-human angiotensin-converting enzyme 2(ACE2) 2 : this was soon confirmed in vitro 4,12 and using structural biology 12,13 . However, SARS-CoV-2 differs from SARS-CoV in two fundamental ways 14 . First, there are six amino acid positions in the receptor-binding domain (RBD) of the spike protein that mediate the attachment of the SARS-CoV and SARS-CoV-2 spike proteins to the human ACE2 receptor 15 . However, amino acids at five of the six positions differed between SARS-CoV and SARS- 14 ). Notably, such differences caused SARS-CoV-2 to have a higher binding avidity to the human ACE2 receptor 11 , and may have contributed to the higher transmissibility of SARS-CoV-2 compared with SARS-CoV. Second, there is a 12-nucleotide (nt) insertion at the cleavage site of the

Background Train is a common mode of public transport across the globe; however, the risk of COVID-19 transmission among individual train passengers remains unclear. Methods We quantified the transmission risk of COVID-19 on high-speed train passengers using data from 2,334 index patients and 72,093 close contacts who had co-travel times of 0–8 hours from 19 December 2019 through 6 March 2020 in China. We analysed the spatial and temporal distribution of COVID-19 transmission among train passengers to elucidate the associations between infection, spatial distance, and co-travel time. Results The attack rate in train passengers on seats within a distance of 3 rows and 5 columns of the index patient varied from 0 to 10.3% (95% confidence interval [CI] 5.3% – 19.0%), with a mean of 0.32% (95%CI 0.29% – 0.37%). Passengers in seats on the same row as the index patient had an average attack rate of 1.5% (95%CI 1.3% – 1.8%), higher than that in other rows (0.14%, 95%CI 0.11% – 0.17%), with a relative risk (RR) of 11.2 (95%CI 8.6 –14.6). Travellers adjacent to the index patient had the highest attack rate (3.5%, 95%CI 2.9% – 4.3%) of COVID-19 infections (RR 18.0, 95%CI 13.9 – 23.4) among all seats. The attack rate decreased with increasing distance, but it increased with increasing co-travel time. The attack rate increased on average by 0.15% (p = 0.005) per hour of co-travel; for passengers at adjacent seats, this increase was 1.3% (p = 0.008), the highest among all seats considered. Conclusions COVID-19 has a high transmission risk among train passengers, but this risk shows significant differences with co-travel time and seat location. During disease outbreaks, when travelling on public transportation in confined spaces such as trains, measures should be taken to reduce the risk of transmission, including increasing seat distance, reducing passenger density, and use of personal hygiene protection.

Brucellosis, a zoonotic disease, was made statutorily notifiable in China in 1955. We analyzed the incidence and spatial–temporal distribution of human brucellosis during 1955–2014 in China using notifiable surveillance data: aggregated data for 1955–2003 and individual case data for 2004–2014. A total of 513,034 brucellosis cases were recorded, of which 99.3% were reported in northern China during 1955–2014, and 69.1% (258, 462/374, 141) occurred during February–July in 1990–2014. Incidence remained high during 1955–1978 (interquartile range 0.42–1.0 cases/100,000 residents), then decreased dramatically in 1979–1994. However, brucellosis has reemerged since 1995 (interquartile range 0.11–0.23 in 1995–2003 and 1.48–2.89 in 2004–2014); the historical high occurred in 2014, and the affected area expanded from northern pastureland provinces to the adjacent grassland and agricultural areas, then to southern coastal and southwestern areas. Control strategies in China should be adjusted to account for these changes by adopting a One Health approach.