Limits...
Detecting differential transmissibilities that affect the size of self-limited outbreaks.

Blumberg S, Funk S, Pulliam JR - PLoS Pathog. (2014)

Bottom Line: Our analysis is based on a branching process model that permits statistical comparison of both the strength and heterogeneity of transmission for two distinct types of cases.When applied to chain size data for MERS-CoV transmission before 2014, our method indicates that despite an apparent trend towards improved control, there is not enough statistical evidence to indicate that R eff has declined with time.Human monkeypox, on the other hand, does not show evidence of differential transmission between animals in contact with humans, primary cases, or secondary cases, which assuages the concern that social mixing can amplify transmission by secondary cases.

View Article: PubMed Central - PubMed

Affiliation: Francis I. Proctor Foundation, University of California San Francisco, San Francisco, California, United States of America; Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.

ABSTRACT
Our ability to respond appropriately to infectious diseases is enhanced by identifying differences in the potential for transmitting infection between individuals. Here, we identify epidemiological traits of self-limited infections (i.e. infections with an effective reproduction number satisfying [0 < R eff < 1) that correlate with transmissibility. Our analysis is based on a branching process model that permits statistical comparison of both the strength and heterogeneity of transmission for two distinct types of cases. Our approach provides insight into a variety of scenarios, including the transmission of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in the Arabian peninsula, measles in North America, pre-eradication smallpox in Europe, and human monkeypox in the Democratic Republic of the Congo. When applied to chain size data for MERS-CoV transmission before 2014, our method indicates that despite an apparent trend towards improved control, there is not enough statistical evidence to indicate that R eff has declined with time. Meanwhile, chain size data for measles in the United States and Canada reveal statistically significant geographic variation in R eff, suggesting that the timing and coverage of national vaccination programs, as well as contact tracing procedures, may shape the size distribution of observed infection clusters. Infection source data for smallpox suggests that primary cases transmitted more than secondary cases, and provides a quantitative assessment of the effectiveness of control interventions. Human monkeypox, on the other hand, does not show evidence of differential transmission between animals in contact with humans, primary cases, or secondary cases, which assuages the concern that social mixing can amplify transmission by secondary cases. Lastly, we evaluate surveillance requirements for detecting a change in the human-to-human transmission of monkeypox since the cessation of cross-protective smallpox vaccination. Our studies lay the foundation for future investigations regarding how infection source, vaccination status or other putative transmissibility traits may affect self-limited transmission.

No MeSH data available.


Related in: MedlinePlus

Power to detect a change in  for human monkeypox following smallpox eradication.A) Number of observed chains of transmission for monkeypox needed to detect a change in  relative to 1980–1984. The 1980–1984 monkeypox data (, ) are compared against a set of simulations with  and , with  specified on the x-axis. This procedure was repeated 1000 times for each value of the number of simulated chains,  (as specified by the y-axis). For each value of , the blue lines indicate the lowest number of observations for which a given power (as a proportion of the 1000 simulations) was achieved. The shades of blue (see legend) indicate different levels of power for which this was done. The straight red line corresponds to the mean number of chains that would have been observed for the 760 case detected during the 2005–2007 monkeypox surveillance [56] for different values of . This line corresponds to  chains (since the average chain size is [20]). B) The power of the 2005–2007 monkeypox surveillance data to detect a change in  for monkeypox. The black dots are the results of simulations, the blue line is a smooth fit to these. This panel corresponds to a cross-section of the figure in panel A along the red line.
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ppat-1004452-g007: Power to detect a change in for human monkeypox following smallpox eradication.A) Number of observed chains of transmission for monkeypox needed to detect a change in relative to 1980–1984. The 1980–1984 monkeypox data (, ) are compared against a set of simulations with and , with specified on the x-axis. This procedure was repeated 1000 times for each value of the number of simulated chains, (as specified by the y-axis). For each value of , the blue lines indicate the lowest number of observations for which a given power (as a proportion of the 1000 simulations) was achieved. The shades of blue (see legend) indicate different levels of power for which this was done. The straight red line corresponds to the mean number of chains that would have been observed for the 760 case detected during the 2005–2007 monkeypox surveillance [56] for different values of . This line corresponds to chains (since the average chain size is [20]). B) The power of the 2005–2007 monkeypox surveillance data to detect a change in for monkeypox. The black dots are the results of simulations, the blue line is a smooth fit to these. This panel corresponds to a cross-section of the figure in panel A along the red line.

Mentions: Due to logistical barriers and the rare nature of the disease, acquiring data on monkeypox is a challenge [42], [56]. In the wake of smallpox eradication, the infrastructure for monkeypox surveillance in 1980–1984 was strong and well funded [42]. The detailed transmission data from this surveillance effort provide an estimate of 0.30 for (95% CI: 0.21–0.42) and 0.33 for (95% CI: 0.17–0.75) [20]. For the 2005–2007 surveillance effort, specific data on cluster sizes and individual-level transmission are unavailable, so an assessment of cannot be made. However, we can quantify the amount of data that would be needed in order to detect a change in relative to 1980–1984 [42], [43], [49]. Simulations show that 200 clusters would provide 70% power to detect an increase in from 0.3 to 0.5 (Figure 7A). As the number of observations increase, smaller changes are more readily noticeable.


Detecting differential transmissibilities that affect the size of self-limited outbreaks.

Blumberg S, Funk S, Pulliam JR - PLoS Pathog. (2014)

Power to detect a change in  for human monkeypox following smallpox eradication.A) Number of observed chains of transmission for monkeypox needed to detect a change in  relative to 1980–1984. The 1980–1984 monkeypox data (, ) are compared against a set of simulations with  and , with  specified on the x-axis. This procedure was repeated 1000 times for each value of the number of simulated chains,  (as specified by the y-axis). For each value of , the blue lines indicate the lowest number of observations for which a given power (as a proportion of the 1000 simulations) was achieved. The shades of blue (see legend) indicate different levels of power for which this was done. The straight red line corresponds to the mean number of chains that would have been observed for the 760 case detected during the 2005–2007 monkeypox surveillance [56] for different values of . This line corresponds to  chains (since the average chain size is [20]). B) The power of the 2005–2007 monkeypox surveillance data to detect a change in  for monkeypox. The black dots are the results of simulations, the blue line is a smooth fit to these. This panel corresponds to a cross-section of the figure in panel A along the red line.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4214794&req=5

ppat-1004452-g007: Power to detect a change in for human monkeypox following smallpox eradication.A) Number of observed chains of transmission for monkeypox needed to detect a change in relative to 1980–1984. The 1980–1984 monkeypox data (, ) are compared against a set of simulations with and , with specified on the x-axis. This procedure was repeated 1000 times for each value of the number of simulated chains, (as specified by the y-axis). For each value of , the blue lines indicate the lowest number of observations for which a given power (as a proportion of the 1000 simulations) was achieved. The shades of blue (see legend) indicate different levels of power for which this was done. The straight red line corresponds to the mean number of chains that would have been observed for the 760 case detected during the 2005–2007 monkeypox surveillance [56] for different values of . This line corresponds to chains (since the average chain size is [20]). B) The power of the 2005–2007 monkeypox surveillance data to detect a change in for monkeypox. The black dots are the results of simulations, the blue line is a smooth fit to these. This panel corresponds to a cross-section of the figure in panel A along the red line.
Mentions: Due to logistical barriers and the rare nature of the disease, acquiring data on monkeypox is a challenge [42], [56]. In the wake of smallpox eradication, the infrastructure for monkeypox surveillance in 1980–1984 was strong and well funded [42]. The detailed transmission data from this surveillance effort provide an estimate of 0.30 for (95% CI: 0.21–0.42) and 0.33 for (95% CI: 0.17–0.75) [20]. For the 2005–2007 surveillance effort, specific data on cluster sizes and individual-level transmission are unavailable, so an assessment of cannot be made. However, we can quantify the amount of data that would be needed in order to detect a change in relative to 1980–1984 [42], [43], [49]. Simulations show that 200 clusters would provide 70% power to detect an increase in from 0.3 to 0.5 (Figure 7A). As the number of observations increase, smaller changes are more readily noticeable.

Bottom Line: Our analysis is based on a branching process model that permits statistical comparison of both the strength and heterogeneity of transmission for two distinct types of cases.When applied to chain size data for MERS-CoV transmission before 2014, our method indicates that despite an apparent trend towards improved control, there is not enough statistical evidence to indicate that R eff has declined with time.Human monkeypox, on the other hand, does not show evidence of differential transmission between animals in contact with humans, primary cases, or secondary cases, which assuages the concern that social mixing can amplify transmission by secondary cases.

View Article: PubMed Central - PubMed

Affiliation: Francis I. Proctor Foundation, University of California San Francisco, San Francisco, California, United States of America; Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.

ABSTRACT
Our ability to respond appropriately to infectious diseases is enhanced by identifying differences in the potential for transmitting infection between individuals. Here, we identify epidemiological traits of self-limited infections (i.e. infections with an effective reproduction number satisfying [0 < R eff < 1) that correlate with transmissibility. Our analysis is based on a branching process model that permits statistical comparison of both the strength and heterogeneity of transmission for two distinct types of cases. Our approach provides insight into a variety of scenarios, including the transmission of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in the Arabian peninsula, measles in North America, pre-eradication smallpox in Europe, and human monkeypox in the Democratic Republic of the Congo. When applied to chain size data for MERS-CoV transmission before 2014, our method indicates that despite an apparent trend towards improved control, there is not enough statistical evidence to indicate that R eff has declined with time. Meanwhile, chain size data for measles in the United States and Canada reveal statistically significant geographic variation in R eff, suggesting that the timing and coverage of national vaccination programs, as well as contact tracing procedures, may shape the size distribution of observed infection clusters. Infection source data for smallpox suggests that primary cases transmitted more than secondary cases, and provides a quantitative assessment of the effectiveness of control interventions. Human monkeypox, on the other hand, does not show evidence of differential transmission between animals in contact with humans, primary cases, or secondary cases, which assuages the concern that social mixing can amplify transmission by secondary cases. Lastly, we evaluate surveillance requirements for detecting a change in the human-to-human transmission of monkeypox since the cessation of cross-protective smallpox vaccination. Our studies lay the foundation for future investigations regarding how infection source, vaccination status or other putative transmissibility traits may affect self-limited transmission.

No MeSH data available.


Related in: MedlinePlus