Using modelling to disentangle the relative contributions of zoonotic and anthroponotic transmission: the case of lassa fever.

BACKGROUND: Zoonotic infections, which transmit from animals to humans, form the majority of new human pathogens. Following zoonotic transmission, the pathogen may already have, or may acquire, the ability to transmit from human to human. With infections such as Lassa fever (LF), an often fatal, rodent-borne, hemorrhagic fever common in areas of West Africa, rodent-to-rodent, rodent-to-human, human-to-human and even human-to-rodent transmission patterns are possible. Indeed, large hospital-related outbreaks have been reported. Estimating the proportion of transmission due to human-to-human routes and related patterns (e.g. existence of super-spreaders), in these scenarios is challenging, but essential for planned interventions. METHODOLOGY/PRINCIPAL FINDINGS: Here, we make use of an innovative modeling approach to analyze data from published outbreaks and the number of LF hospitalized patients to Kenema Government Hospital in Sierra Leone to estimate the likely contribution of human-to-human transmission. The analyses show that almost [Formula: see text] of the cases at KGH are secondary cases arising from human-to-human transmission. However, we found much of this transmission is associated with a disproportionally large impact of a few individuals ('super-spreaders'), as we found only [Formula: see text] of human cases result in an effective reproduction number (i.e. the average number of secondary cases per infectious case) [Formula: see text], with a maximum value up to [Formula: see text]. CONCLUSIONS/SIGNIFICANCE: This work explains the discrepancy between the sizes of reported LF outbreaks and a clinical perception that human-to-human transmission is low. Future assessment of risks of LF and infection control guidelines should take into account the potentially large impact of super-spreaders in human-to-human transmission. Our work highlights several neglected topics in LF research, the occurrence and nature of super-spreading events and aspects of social behavior in transmission and detection.This work for the Dynamic Drivers of Disease in Africa Consortium, NERC project no. NE-J001570-1, was funded with support from the Ecosystem Services for Poverty Alleviation (ESPA) programme. The ESPA programme is funded by the Department for International Development (DFID), the Economic and Social Research Council (ESRC) and the Natural Environment Research Council (NERC). See more at: http://www.espa.ac.uk/about/identity/acknowledging-espafunding# sthash.UivKPObf.dpuf. GL, JLNW, AAC, CTW and EFC also benefit from the support of the small mammal disease working group, funded by the Research and Policy for Infectious Disease Dynamics (RAPIDD) programme of the Science and Technology Directorate, Department of Homeland Security, and Fogarty International Center, USA. JLNW and AC were also supported by the European Union FP7 project ANTIGONE (contract number 278976). AAC is supported by a Royal Society Wolfson Reearch Merit Award. JLNW is also supported by the Alborada Trust. JSS, LM, RG, and JGS were supported by the US National Institute of Health (JSS: NIH grant P20GM103501; LM, RG, JGS: NIH grant BAA-NIAID-DAIT-NIHQI2008031).This is the final published version. It first appeared at http://www.plosntds.org/article/info%3Adoi%2F10.1371%2Fjournal.pntd.0003398

Using Modelling to Disentangle the Relative Contributions of Zoonotic and Anthroponotic Transmission: The Case of Lassa Fever

Background Zoonotic infections, which transmit from animals to humans, form the majority of new human pathogens. Following zoonotic transmission, the pathogen may already have, or may acquire, the ability to transmit from human to human. With infections such as Lassa fever (LF), an often fatal, rodent-borne, hemorrhagic fever common in areas of West Africa, rodent-to-rodent, rodent-to-human, human-to-human and even human-to-rodent transmission patterns are possible. Indeed, large hospital-related outbreaks have been reported. Estimating the proportion of transmission due to human-to-human routes and related patterns (e.g. existence of super-spreaders), in these scenarios is challenging, but essential for planned interventions. Methodology/Principal Findings Here, we make use of an innovative modeling approach to analyze data from published outbreaks and the number of LF hospitalized patients to Kenema Government Hospital in Sierra Leone to estimate the likely contribution of human-to-human transmission. The analyses show that almost of the cases at KGH are secondary cases arising from human-to-human transmission. However, we found much of this transmission is associated with a disproportionally large impact of a few individuals (‘super-spreaders’), as we found only of human cases result in an effective reproduction number (i.e. the average number of secondary cases per infectious case) , with a maximum value up to . Conclusions/Significance This work explains the discrepancy between the sizes of reported LF outbreaks and a clinical perception that human-to-human transmission is low. Future assessment of risks of LF and infection control guidelines should take into account the potentially large impact of super-spreaders in human-to-human transmission. Our work highlights several neglected topics in LF research, the occurrence and nature of super-spreading events and aspects of social behavior in transmission and detection.</p

High heart rate at admission as a predictive factor of mortality in hospitalized patients with Lassa fever: An observational cohort study in Sierra Leone

International audienc

Epidemic curve.

<p>Daily number of referred/visiting patients at KGH (confirmed cases only) from the of April to the of January , <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003398#pntd.0003398-Shaffer1" target="_blank">[1]</a>.</p

Impact of super-spreaders I.

<p>A: Distribution of all individual for both nosocomial outbreaks, based on the permutations of the duration of illness. Mean value of the joint data: , median: , maximum: , proportion of cases when : , proportion of cases when : . B: Distribution of the effective reproduction number for cases of hospitalized patients in KGH for different values of the contribution of human-to-human transmission, , the corresponding data for the extra-nosocomial ( permutation based on , , , , cases in Jos) and all nosocomial outbreaks (based on all Jos and Zorzor cases) are also shown. C: Distribution of the total effective reproduction number, <i>i.e.</i> the average number of cases during the entire duration of the epidemic for different values the contribution of human-to-human transmission, .</p

Impact of super-spreaders II.

<p>A: proportion of cases when the individual effective reproduction number is greater than one. (<i>i.e.</i> the ratio of the cardinalities of and , where is set of all simulated and the subset of cases when is greater than one). B: the expected, relative number of cases generated by this proportion. (<i>i.e.</i> the fraction of the areas of )</p

Nosocomial outbreaks.

<p>A: Diagrammatic representation of LF cases admitted at Jos Hospital, Nigeria (total duration of the outbreak days), showing period of illness and interrelation among patients <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003398#pntd.0003398-Carey1" target="_blank">[2]</a>. The horizontal bars represent each patient. The x-axis is the time expressed in days from the start of the outbreak, when TS developed the illness (thus time in the calculation corresponds to December 1969). The grey portion of the bars are the period between the onset of the symptoms and admission to hospital; the black portion of the bars are the period between admission to hospital and discharge/death of the patients; the red thin lines are the period of exposure to the index case TS. The green bar represent the time when the patient was at the ward for unrelated illness. Note, the same diagram in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003398#pntd.0003398-Carey1" target="_blank">[2]</a> present an extra case, JT, which is not included here. This case refers to Dr. Jeanette M. Troup one of the first scientists working on Lassa Fever Virus, who contracted the disease from an autopsy accident incurred during examination of one of the fatal cases. B: Diagrammatic representation of LF cases admitted at Zorzor Hospital (total duration of the outbreak days), Liberia, showing period of illness and interrelation among patients <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003398#pntd.0003398-Monath1" target="_blank">[3]</a>. C: As in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003398#pntd-0003398-g001" target="_blank">Fig. 1.A</a>, but the periods of illness (symptoms plus time at hospital) are randomly permuted. The contact network is kept the same. D: An example of how the time was calculated. In this particular case if and otherwise, where is the time when case is no longer exposed to case .</p

Individual effective reproduction number and generation time.

<p>Box-plot for the individual for the nosocomial outbreak described in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003398#pntd.0003398-Carey1" target="_blank">[2]</a> based on the permutations of the duration of illness. It shows the first and third percentiles, the minimum and maximum values, the median, and outliers (red dots). The dashed line represents the case when the effective reproduction number is equal to . A: nosocomial outbreak in Jos <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003398#pntd.0003398-Carey1" target="_blank">[2]</a>. B: nosocomial outbreak in Zorzor <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003398#pntd.0003398-Monath1" target="_blank">[3]</a>. C: Distribution of generation time for the two nosocomial outbreaks. The statistics are based on the permutations of the duration of illness. D: Distribution of generation time for extra-nosocomial cases. The statistics are based on the permutations of the duration of illness.</p