The transmissibility of novel Coronavirus in the early stages of the 2019-20 outbreak in Wuhan: Exploring initial point-source exposure sizes and durations using scenario analysis.

Background: The current novel coronavirus outbreak appears to have originated from a point-source exposure event at Huanan seafood wholesale market in Wuhan, China. There is still uncertainty around the scale and duration of this exposure event. This has implications for the estimated transmissibility of the coronavirus and as such, these potential scenarios should be explored.   Methods: We used a stochastic branching process model, parameterised with available data where possible and otherwise informed by the 2002-2003 Severe Acute Respiratory Syndrome (SARS) outbreak, to simulate the Wuhan outbreak. We evaluated scenarios for the following parameters: the size, and duration of the initial transmission event, the serial interval, and the reproduction number (R0). We restricted model simulations based on the number of observed cases on the 25th of January, accepting samples that were within a 5% interval on either side of this estimate. Results: Using a pre-intervention SARS-like serial interval suggested a larger initial transmission event and a higher R0 estimate. Using a SARs-like serial interval we found that the most likely scenario produced an R0 estimate between 2-2.7 (90% credible interval (CrI)). A pre-intervention SARS-like serial interval resulted in an R0 estimate between 2-3 (90% CrI). There were other plausible scenarios with smaller events sizes and longer duration that had comparable R0 estimates. There were very few simulations that were able to reproduce the observed data when R0 was less than 1. Conclusions: Our results indicate that an R0 of less than 1 was highly unlikely unless the size of the initial exposure event was much greater than currently reported. We found that R0 estimates were comparable across scenarios with decreasing event size and increasing duration. Scenarios with a pre-intervention SARS-like serial interval resulted in a higher R0 and were equally plausible to scenarios with SARs-like serial intervals

Effectiveness of airport screening at detecting travellers infected with novel coronavirus (2019-nCoV).

We evaluated effectiveness of thermal passenger screening for 2019-nCoV infection at airport exit and entry to inform public health decision-making. In our baseline scenario, we estimated that 46% (95% confidence interval: 36 to 58) of infected travellers would not be detected, depending on incubation period, sensitivity of exit and entry screening, and proportion of asymptomatic cases. Airport screening is unlikely to detect a sufficient proportion of 2019-nCoV infected travellers to avoid entry of infected travellers

Effectiveness of airport-based interventions at detecting travellers and delaying an outbreak of COVID-19 (formerly 2019-nCoV)

An online tool and study to assess the effectiveness of airport screenin

UK Colocation Dashboard

Facebook uses anonymized mobile phone location data to understand the probability of "colocation" between administrative areas over time -- a proxy for social contact. Use this dashboard to explore the network of colocation probabilities in the UK. Colocation is defined as two individuals within the same ~0.6 by ~0.6 km area during the same five minute period in a given week

Probability of a large 2019-nCoV outbreak following introduction of cases

This analysis uses a model that incorporates randomness and individual-level variation in transmission (i.e. potential for 'superspreading') to calculate the probability that a given number of independently introduced cases in a new location will eventually lead to a large outbreak. (Source: Lloyd-Smith et al, Nature, 2005). There are two key values to explore: the reproduction number in the new location (i.e. average number of secondary cases generated by a typical infectious individual); and individual-level variation in transmission - do all cases generate similar numbers of secondary cases, or do most generate few and some lots? Infections with more individual-level variation (such as SARS) lead to more fragile initial transmission chains, and hence are overall less likely to spark a large outbreak following an introduced case (although they can lead to rapidly growing outbreaks if by chance transmission does take hold. The random-mixing option in the drop-down menu assumes transmission occurs at random with similar secondary cases from all individuals

thimotei/CFR_calculation

The data required for this analysis is a time-series for both cases and deaths, along with the corresponding delay distribution. We scrape this data from ECDC, using the NCoVUtils package