How many human pathogens are there in Laos? An estimate of national human pathogen diversity and analysis of historical trends

Abstract

Objective: The emergence of infectious diseases pose major global health threats. Estimates of total in-country human pathogen diversity, and insights as to how and when species were described through history, could be used to estimate the probability of new pathogen discoveries. Data from the Lao People's Democratic Republic (Laos) were used in this proof-of-concept study to estimate national human pathogen diversity and to examine historical discovery rate drivers.

Methods: A systematic survey of the French and English scientific and grey literature of pathogen description in Laos between 1874 and 2017 was conducted. The first descriptions of each known human pathogen in Laos were coded according to the diagnostic evidence available. Cumulative frequency of discovery across time informed the rate of discovery. Four distinct periods of health systems development in Laos were identified prospectively and juxtaposed to the unmodelled rate of discovery. A model with a time-varying rate of discovery was fitted to these data using a Markov-Chain- Monte-Carlo technique.

Results: From 6456 pathogen descriptions, 245 discoveries of known human pathogens in Laos, including repeat discoveries using different grades of evidence, were identified. The models estimate that the Laos human pathogen species diversity in 2017 is between 169 and 206. During the last decade, there has been a 33-fold increase in the discovery rate coinciding with the strengthening of medical research and microbiology.

Conclusion: Discovery curves can be used to model and estimate country-level human pathogen diversity present in a territory. Combining this with historical assessment improves the understanding of the factors affecting local pathogen discovery.

Prospero registration number: A protocol of this work was registered on PROSPERO (ID:CRD42016046728).

Keywords: epidemiology; health systems evaluation; mathematical modelling; public health.

Figure 1

The cumulative frequency of human pathogen discovery in Laos for the period 1874–2017, stratified by the grade of evidence used to identify the pathogens, in which grade 1 represents either culture, direct observation, molecular (which includes PCR diagnosis) or direct microscope observation, grade 2 is serological diagnosis and grade 3 is clinical diagnosis.

Figure 2

Discovery curve model for level 1 data and MCMC output the yellow points represent the observed data, the red line is the best fitting model and the black lines represent the uncertainty in the model estimates. Underneath the model fit curve are a series of histograms which represent the posterior distributions of parameter estimates. The chain output can be seen at the bottom of the figure and represent the range of parameter values tested and the number of model iterations. MCMC, Markov-Chain-Monte-Carlo.

Figure 3

Discovery curve model for level 2 data and MCMC output. The yellow points represent the observed data, the red line is the best fitting model and the black-lines represent the uncertainty in the model estimates. Underneath the model are a series of histograms which represent the posterior distributions of parameter estimates. The chain output can be seen at the bottom of the figure and represent the range of parameter values tested and the number of iterations. MCMC, Markov-Chain-Monte-Carlo.

Figure 4

Discovery curve model for level 3 data and MCMC output. The yellow points represent the observed data, the red line is the best fitting model and the black lines represent the uncertainty in the model estimates. Underneath the model are a series of histograms which represent the posterior distributions of parameter estimates. The chain output can be seen at the bottom of the figure and represent the range of parameter values tested and the number of iterations. MCMC, Markov-Chain-Monte-Carlo.