However, the report is
1. Trying really hard to conflate/confound that fact with the relative ineffectiveness of enforced quarantine (referred to as cordons sanitaire) in response to that accelerated urban infection spread; and
2. Trying really hard to avoid even the word fomite.
The best they come to touching fomite Ebola spread is mentioning the “Homecare setting.”
See the link for more text and tables.
SEPTEMBER 18, 2014 · RESEARCH
AUTHORS
Sherry Towers
Oscar Patterson-Lomba
Carlos Castillo-Chavez
ABSTRACT
Background
The rapidly evolving 2014 Ebola virus disease (EVD) outbreak in West Africa is the largest documented in history, both in terms of the number of people infected and in the geographic spread. The high morbidity and mortality have inspired response strategies to the outbreak at the individual, regional, and national levels. Methods to provide real-time assessment of changing transmission dynamics are critical to the understanding of how these adaptive intervention measures have affected the spread of the outbreak.
Methods
In this analysis, we use the time series of EVD cases in Guinea, Sierra Leone, and Liberia up to September 8, 2014, and employ novel methodology to estimate how the rate of exponential rise of new cases has changed over the outbreak using piecewise fits of exponential curves to the outbreak data.
Results
We find that for Liberia and Guinea, the effective reproduction number rose, rather than fell, around the time that the outbreak spread to densely populated cities, and enforced quarantine was imposed on several regions in the countries; this may indicate that enforced quarantine may not be an effective control measure.
Conclusions
If effective control measures are not put in place, and the current rate of exponential rise of new cases continues, we predict 4400 new Ebola cases in West Africa during the last half of the month of September, with an upper 95% confidence level of 6800 new cases.
FUNDING STATEMENT
This publication was made possible by grant number 1R01GM100471-01 from the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NIGMS. OPL additionally gratefully acknowledges the support of NIH training grant T32AI007358-26. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
INTRODUCTION
Ebola virus disease (EVD) is a severe, often fatal viral infection in humans, with a case fatality risk (CFR) of up to 90% in previous outbreaks 1,2,3,13,14. There have been 13 recorded outbreaks since 2000, associated with at least two distinct strains of the Ebola virus (Ebola-Zaire and the Ebola-Sudan) 23, but the current West African outbreak has spread over the largest geographic area and caused the greatest number of infections and deaths 3,15,16,22,23. Critically, unlike previous epidemics, this outbreak has spread to densely populated areas, increasing the risk of international spread 22; the problem is compounded by lack of resources for effective quarantine and isolation in the under-developed countries that have been affected, and the high mobility of the population in a region with porous borders.
The spread of EVD requires direct contact with infected blood, tissue, or body fluids of either the living ill or the recently deceased, and the disease is particularly prone to transmission in unprotected homecare settings and during traditional burials 24,25. Symptoms typically appear after 2–21 days, and the disease quickly progresses and kills most infected patients within a few days 13,14. No licensed vaccine or specific treatment is currently available 26, leaving improved hygiene, quarantine, isolation, and social distancing as the only potential interventions. In an attempt to quell the outbreak, on August 1st the governments of Guinea, Liberia, and Sierra Leone announced plans to impose a military-enforced mass quarantine of entire regions and villages , in an attempt to prevent spread of the disease (referred to as cordons sanitaire) to other areas 17. The spread of disease within the isolated areas has typically been allowed to run its course 19,20.
Assessing the net positive or negative impact of such attempted intervention measures involves assessment of a complex adaptive system, where the system dynamics are intimately connected to the impact of multiple feedback mechanisms that evolve over time 9. Because the situation in West Africa is rapidly evolving in time, and attempts at intervention measures are far ranging and also rapidly evolving, understanding how best to control the outbreak, and/or assessing whether or not current attempts to contain the spread are working, poses unique challenges.
In this analysis, we examine current outbreak incidence data for Guinea, Sierra Leone, and Liberia up to September 8, 2014, as estimated by the World Health Organization (WHO) 3,4. Using novel methodology, we assess the impact of adaptive response measures by fitting piecewise exponential curves along the data time series to estimate the evolving rate of exponential rise (or decline) in cases. We then use a Susceptible, Exposed, Infected, Recovered (SEIR) model to estimate the temporal patterns of the effective reproduction number for the outbreak in each country. In the following sections, we describe our sources of data, and the statistical and modeling methods used to estimate the effective reproduction number.
[big snip]
Estimation of the effective reproduction number from the local rates of exponential rise
The basic reproduction number, R0, of an infectious disease is the average number of secondary cases a case generates during the course of its infectious period, in a completely susceptible population. The basic reproduction number is affected not only by the ease at which an infection is transmitted upon contact between two hosts, but also by the contact rate between members of the population. Because of heterogeneity in susceptibility, infectiousness, and differing social dynamics, the estimates of for an infectious disease tend to be different across populations. For example, rural and densely populated urban areas are likely to have substantial differences in contact rates 10. Traditionally R0 has been computed under simplifying assumptions, like the existence of a constant contact structure, or under the assumption that interventions like quarantine and isolation are uniformly applied from the very beginning of the epidemic. However, particularly in the case of a disease with high morbidity and mortality, it has been shown that human behaviors adapt to limit spread of the disease 9, thus limiting the usefulness of R0 as a means to assess the efficacy of evolving intervention strategies, and to forecast the future progression of the outbreak.
Given these limitations of R0 , in this analysis we thus instead assess the time evolution of the effective reproduction number, Reff , which is a dynamic estimate of the average number of secondary cases per infectious case in a population composed of both susceptible and non-susceptible individuals during the course of an outbreak. The effective reproduction number is a function of, among other things, the probability of transmission upon contact of the disease, the contact rate within the population, and the number of susceptible individuals in the population, all of which may be time dependent.
In an epidemic, given a short enough period of time, the value of Reff during that time period can be considered to be approximately constant.
[snip]
Typically, if the transmissibility and contact rates in the population remain constant, one expects to see the effective reproduction number decrease in time as the fraction of susceptible individuals in the population decreases. However, if Reff increases, it can signal that, for instance, contact rates within the population have increased, the virus has become more transmissible, and/or that attempted intervention strategies are actually making the outbreak worse.
[big snip]
We stress again that our analysis makes no assumptions about whether or not control measures have been employed, and/or when they were employed. We simply examine the local rates of exponential rise to estimate how the effective reproduction number appears to be changing in time. We note that up until mid-August, the values of for Guinea are significantly below the average central value obtained from the fit the the data from July 1st onwards, and were rising (unless otherwise noted, all subsequent statistical tests are Bonferroni corrected one-sided Z tests, with rejection of the null hypothesis occurring when p < 0.05/k, where k is the number of values being compared). The dip in the two recent Guinea data points compared to the central value is not significant. In addition, the dip in the two recent estimates of from the Liberia data are also not significant. However, the estimates of for the three earliest points in the Liberia local time series are significantly lower than the central value, and rising. None of the estimates of from the Sierra Leone data and combined data for all countries are significantly different than the central value.
It is unclear why the transmission rate of the disease apparently rose for both Guinea and Liberia between mid July to mid August, and why the transmission rate in Sierra Leone is systematically lower, although is important to note that the WHO data are obtained from rudimentary surveillance systems in under-developed countries, under the stress of a rapidly evolving outbreak situation. The temporal patterns we observe may thus partly be due to variations in surveillance during the outbreak, under-reporting, and/or reporting delays. In addition, serial passage of the disease as the outbreak progresses may be leading to increased pathogenicity, and a subsequent increasingly larger rate of increase in case counts. However, it also must be considered that otherwise well intentioned attempts at intervention may in fact be making the situation worse, at least in some regions; in a joint meeting of officials from Guinea, Liberia, and Sierra Leone on Aug 1st , it was announced that cordons sanitaire would be implemented, to seal off the villages and regions worst hit by the outbreak, in an attempt to limit its spread outside those areas 17. In addition, at that time Liberia closed all schools and non-essential government offices 18, and two weeks later imposed a military-enforced cordon sanitaire on the West Point slum of Monrovia, sparking riots 19. It must be stressed that cordons sanitaire do not generally attempt to limit disease spread within the quarantined areas, and the implementation of these measures in West Africa has received criticism due to the fact that the quarantined areas are at risk for crowding, lack of medical and basic services, and poor sanitation, potentially increasing the spread of disease within those areas 20. It was around the period of time that the cordons sanitaire were first imposed, and when the outbreak moved to the densely populated cities of Conakry and Monrovia, that the exponential rise in new cases in Guinea and Liberia increased, rather than went down. Again, it is unclear why the rate of exponential rise in Sierra Leone was apparently unaffected by these events.
Based on our estimates of the exponential rise in cases between July 1st to the beginning of September, if this rise is to continue unabated, there will be approximately 4400 new EVD cases in West Africa during the last half of the month of September (95% CI [3000, 6800]), 500, 900, and 3000 of which will be in Sierra Leone, Guinea, and Liberia, respectively.
SUMMARY
We have presented an analysis where piecewise exponential fits are employed to estimate the local rate of exponential rise in cases in the 2014 West Africa Ebola outbreak. We have shown that these local rates of exponential rise can be used in conjunction with a mathematical model to estimate the temporal changes in the effective reproduction number (in the case of this analysis, we employed an SEIR model). Our analysis indicates that the spread of the disease to densely populated cities, and/or the imposition of cordons sanitaire in West Africa may have accelerated the spread of the disease in some regions. Our methodology is novel, and is applicable to any outbreak, and any mathematical disease model.
Hmmmm. I don’t know if now is the time to switch to a “novel” mathematical approach to R0. I’m also struck by the fact that they based their model on WHO numbers, acknowledge that those numbers are not reliable, yet in the end say their model can be used for any outbreak of any disease.
Very interesting, thank you!