Constructing Ebola transmission chains from West Africa and estimating model parameters using internet sources

Abstract

SUMMARY During the recent Ebola crisis in West Africa, individual person-level details of disease onset, transmissions, and outcomes such as survival or death were reported in online news media. We set out to document disease transmission chains for Ebola, with the goal of generating a timely account that could be used for surveillance, mathematical modeling, and public health decision-making. By accessing public web pages only, such as locally produced newspapers and blogs, we created a transmission chain involving two Ebola clusters in West Africa that compared favorably with other published transmission chains, and derived parameters for a mathematical model of Ebola disease transmission that were not statistically different from those derived from published sources. We present a protocol for responsibly gleaning epidemiological facts, transmission model parameters, and useful details from affected communities using mostly indigenously produced sources. After comparing our transmission parameters to published parameters, we discuss additional benefits of our method, such as gaining practical information about the affected community, its infrastructure, politics, and culture. We also briefly compare our method to similar efforts that used mostly non-indigenous online sources to generate epidemiological information.

What effect might border screening have on preventing importation of COVID-19 compared with other infections? A modelling study

“…If we assume linearity in our results, we find that, by our model, every extra hour travelled increases rate of detection but 0.2505%, 0.0633% and 0.0028% for the 0 - 14 day, 0 - 7 day and 0 - 3 day exposure windows respectively, representing a significant difference. This difference most likely stems from the distribution of incubation periods, which in [13], is quoted as having a mean of 9.1 days with a SD of 7.3, with no indication as to how this is distributed, compared to our distribution (taken from [16]) which has mean of 12.5 days and a SD of 4.35.…”

“…Tabulated results of border screening model’s output (right column) for varying parameter inputs. The parameterisation of the disease incubation distributions of Ebola[10], SARS[11] and Influenza[12] have been calculated/taken from existing academic literature (see supplementary text)…”

Investigating the potential benefit that requiring travellers to self-isolate on arrival may have upon the reducing of case importations during international outbreaks of influenza, SARS, Ebola virus disease and COVID-19

“…on vacation), or more longer term (such as being a resident). Flight times are sampled from the ranges [3,5], [7,9] and [11,13] to model where travellers are required to take a short, medium or long-haul flight from country A to country B. Lastly, we consider quarantine periods which involves either 3, 5, 7, 10 or 14 days of self-isolation.…”

“…These times are sampled from the distributions D exp , D inc and D flight respectively, each of which varies depending on the scenario being considered. The incubation distributions for influenza, SARS, COVID-19 and Ebola have been taken from the works [2]–[5] respectively, just as in [1]. To extend our previous work, travellers that successfully arrive into country B undetected must then undergo self-isolation for a fixed period, T iso .…”

“…• The flight time for each traveller is sampled from a uniform distribution, flight , over the intervals [3,5], [7,9] or [11,13] depending on whether a short, medium or long-haul flight is being considered. • All travellers that fly to country B are tested on arrival.…”