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

Lo Iacono G, Cunningham AA, Fichet-Calvet E, Garry RF, Grant DS, Khan SH, Leach M, Moses LM, Schieffelin JS, Shaffer JG, Webb CT, Wood JL - PLoS Negl Trop Dis (2015)

Bottom Line: Zoonotic infections, which transmit from animals to humans, form the majority of new human pathogens.Indeed, large hospital-related outbreaks have been reported.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].

View Article: PubMed Central - PubMed

Affiliation: Department of Veterinary Medicine, Disease Dynamics Unit, University of Cambridge, Cambridge, United Kingdom.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus

Individual effective reproduction number and generation time.Box-plot for the individual  for the nosocomial outbreak described in [2] 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 [2]. B: nosocomial outbreak in Zorzor [3]. 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.
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pntd-0003398-g003: Individual effective reproduction number and generation time.Box-plot for the individual for the nosocomial outbreak described in [2] 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 [2]. B: nosocomial outbreak in Zorzor [3]. 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.

Mentions: The distribution of the quantity is interpreted as the distribution of the generation time, i.e. the time between a primary case and a secondary case, and it is presented in Figs. 3.C and 3.D (see also Figures S2, S3, S4 and S5 in S2 Text).


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

Lo Iacono G, Cunningham AA, Fichet-Calvet E, Garry RF, Grant DS, Khan SH, Leach M, Moses LM, Schieffelin JS, Shaffer JG, Webb CT, Wood JL - PLoS Negl Trop Dis (2015)

Individual effective reproduction number and generation time.Box-plot for the individual  for the nosocomial outbreak described in [2] 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 [2]. B: nosocomial outbreak in Zorzor [3]. 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.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4288732&req=5

pntd-0003398-g003: Individual effective reproduction number and generation time.Box-plot for the individual for the nosocomial outbreak described in [2] 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 [2]. B: nosocomial outbreak in Zorzor [3]. 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.
Mentions: The distribution of the quantity is interpreted as the distribution of the generation time, i.e. the time between a primary case and a secondary case, and it is presented in Figs. 3.C and 3.D (see also Figures S2, S3, S4 and S5 in S2 Text).

Bottom Line: Zoonotic infections, which transmit from animals to humans, form the majority of new human pathogens.Indeed, large hospital-related outbreaks have been reported.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].

View Article: PubMed Central - PubMed

Affiliation: Department of Veterinary Medicine, Disease Dynamics Unit, University of Cambridge, Cambridge, United Kingdom.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus