Incorporating Human Movement Data to Improve Epidemiological Estimates for 2019-nCoV

Abstract

Estimating the key epidemiological features of the novel coronavirus (2019-nCoV) epidemic proves to be challenging, given incompleteness and delays in early data reporting, in particular, the severe under-reporting bias in the epicenter, Wuhan, Hubei Province, China. As a result, the current literature reports widely varying estimates. We developed an alternative geostratified debiasing estimation framework by incorporating human mobility with case reporting data in three stratified zones, i.e., Wuhan, Hubei Province excluding Wuhan, and mainland China excluding Hubei. We estimated the latent infection ratio to be around 0.12% (18,556 people) and the basic reproduction number to be 3.24 in Wuhan before the city's lockdown on January 23, 2020. The findings based on this debiasing framework have important implications to prioritization of control and prevention efforts.One Sentence Summary: A geo-stratified debiasing approach incorporating human movement data was developed to improve modeling of the 2019-nCoV epidemic.All rights reserved. No reuse allowed without permission. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The impact of social distancing and epicenter lockdown on the COVID-19 epidemic in mainland China: A data-driven SEIQR model study

“…Technically, the available population migration data [7] covers the period from Jan 01 to Jan 22, so the timing of Wuhan lockdown investigated in this study is confined within this period.…”

“…Wuhan has a population of around 15.39 million, including a resident population of 9 million, and nearly 6.39 million floating population exported before Jan 23 [7]. The population exported from Wuhan is the main source of imported cases in the rest of China.…”

“…We assumed that the population exported from Wuhan was susceptible and exposed population. During the period of time from Jan 01 to Jan 22, 2020, the number of exported population from Wuhan was derived from two sources, a) domestic passenger number by air, train and road provided by Cao, Z., et al [7], and b) international outbound passenger number by air departed from Tianhe Airport (Wuhan, China) provided by a commercial APP named "Flight Steward". Officially reported case data by the National Health Commission were used to estimate the parameters of SEIQR models.…”

The First 75 Days of Novel Coronavirus (SARS-CoV-2) Outbreak: Recent Advances, Prevention, and Treatment

“…R 0 is an epidemiologic matrix used to describe the transmissibility of infectious agent. Various studies [22,28,[46][47][48][49][50] indicated the number of expected cases directly generated by a single infected case is within the range of 0.8-5.7. As the aforementioned studies approximated the virus incubation period at 5.2-12.5 days, the expected epidemic doubling time of COVID-19 could be in the range of 2.9-8.4 days.…”

“…Most of the above-mentioned studies simulated the Wuhan epidemic dynamic network by utilizing a deterministic susceptible-exposed-infected-recovered (SEIR) model that categorizes the population into four states: susceptible individuals (S), asymptomatic individuals during the incubation period (E), confirmed infectious individuals (I), and recovered individuals (R). A SEIRDC model enhanced from the standard SEIR model by introducing dead and auxiliary variable (DC) [50] is also used for COVID-19 investigation. A geological-stratified debiasing approach is incorporated into the SEIRDC model to include the latent infection ratio (L&I) amongst the people traveling from Wuhan to other destinations in mainland China to improve the overall epidemiological estimation of the coronavirus.…”