Increased frequency of travel in the presence of cross-immunity may act to decrease the chance of a global pandemic

Thompson, Robin N; Thompson, Craig; Pelerman, O; Gupta, Sunetra; Obolski, Uri

doi:10.1098/rstb.2018.0274

Cited by 40 publications

(44 citation statements)

References 59 publications

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“…These, and indeed any factor resulting in changes in pathogen transmissibility (e.g. the depletion of the number of susceptible individuals in the population (Thompson et al, 2019a)), will be reflected in time-dependent reproduction number estimates generated using our approach, so these estimates need to be interpreted carefully when assessing the effectiveness of interventions.…”

Section: Discussionmentioning

confidence: 99%

Improved inference of time-varying reproduction numbers during infectious disease outbreaks

et al. 2019

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A B S T R A C TAccurate estimation of the parameters characterising infectious disease transmission is vital for optimising control interventions during epidemics. A valuable metric for assessing the current threat posed by an outbreak is the time-dependent reproduction number, i.e. the expected number of secondary cases caused by each infected individual. This quantity can be estimated using data on the numbers of observed new cases at successive times during an epidemic and the distribution of the serial interval (the time between symptomatic cases in a transmission chain). Some methods for estimating the reproduction number rely on pre-existing estimates of the serial interval distribution and assume that the entire outbreak is driven by local transmission. Here we show that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: (i) upto-date observations of the serial interval to be included, and; (ii) cases arising from local transmission to be distinguished from those imported from elsewhere. We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2.2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens.

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Section: Discussionmentioning

confidence: 99%

Improved inference of time-varying reproduction numbers during infectious disease outbreaks

et al. 2019

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“…We note that this formula is correct p i /R p i = 1 0,i for an exponential infectious period, which is often implicitly assumed in infectious diseases dynamic models [21] . Nonetheless, we performed a sensitivity analysis on the general case of a gamma distributed infectious period, with the appropriate correction [22], [23] , while varying the shape parameter from the exponential case to a a k = 1 high value of (see supplementary material Figure S2 for details). Results were k = 6 qualitatively similar.…”

Section: Outbreak Probabilitymentioning

confidence: 99%

Estimating COVID-19 outbreak risk through air travel

Daon

Thompson

Obolski

2020

Preprint

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Background : COVID-19 has spread rapidly across the globe during the first several months of 2020, creating a pandemic. Substantial, non-discriminatory limitations have been imposed on air travel to inhibit this spread. As the disease prevalence and incidence will decrease, more specific control measures will be sought so that commercial air travel can continue to operate yet not impose a high threat of COVID-19 resurgence. Methods : We use modelled global air travel data and population density estimates to analyse the risk posed by 1364 airports to initiate a COVID-19 outbreak. We calculate the risk using a probabilistic approach that considers the volume of air travelers between airports and the of each location, scaled by population density. This R 0 exercise is performed globally as well as specifically for two potentially vulnerable locations: Africa and India. : medRxiv preprint passenger travelling from Europe. On the other hand, the highest risk to India is from domestic travellers. Our results are robust to changes in the underlying epidemiological assumptions. Conclusions : Variation in flight volumes and destinations creates a non-uniform distribution of the risk different airports pose to resurgence of a COVID-19 outbreak. We suggest the method presented here as a tool for the estimation of this risk. Our method can be used to inform efficient allocation of resources, such as tests identifying infected passengers, so that they could be differentially deployed in various locations.

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“…The most devastating infectious disease outbreaks are those that have a wide geographical range, as opposed to being confined to a small region [11,12]. The previously known virus that is most similar to the 2019-nCoV is the SARS coronavirus [13], which generated cases in 37 countries in 2002-2003 [13,14].…”

Section: Introductionmentioning

confidence: 99%

Novel Coronavirus Outbreak in Wuhan, China, 2020: Intense Surveillance Is Vital for Preventing Sustained Transmission in New Locations

Thompson

2020

JCM

Self Cite

166

170

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The outbreak of pneumonia originating in Wuhan, China, has generated 24,500 confirmed cases, including 492 deaths, as of 5 February 2020. The virus (2019-nCoV) has spread elsewhere in China and to 24 countries, including South Korea, Thailand, Japan and USA. Fortunately, there has only been limited human-to-human transmission outside of China. Here, we assess the risk of sustained transmission whenever the coronavirus arrives in other countries. Data describing the times from symptom onset to hospitalisation for 47 patients infected early in the current outbreak are used to generate an estimate for the probability that an imported case is followed by sustained human-to-human transmission. Under the assumptions that the imported case is representative of the patients in China, and that the 2019-nCoV is similarly transmissible to the SARS coronavirus, the probability that an imported case is followed by sustained human-to-human transmission is 0.41 (credible interval [0.27, 0.55]). However, if the mean time from symptom onset to hospitalisation can be halved by intense surveillance, then the probability that an imported case leads to sustained transmission is only 0.012 (credible interval [0, 0.099]). This emphasises the importance of current surveillance efforts in countries around the world, to ensure that the ongoing outbreak will not become a global pandemic.

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Increased frequency of travel in the presence of cross-immunity may act to decrease the chance of a global pandemic

Cited by 40 publications

References 59 publications

Improved inference of time-varying reproduction numbers during infectious disease outbreaks

Improved inference of time-varying reproduction numbers during infectious disease outbreaks

Estimating COVID-19 outbreak risk through air travel

Novel Coronavirus Outbreak in Wuhan, China, 2020: Intense Surveillance Is Vital for Preventing Sustained Transmission in New Locations

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