Inference In Epidemiology Research Articles

A data science pipeline applied to Australia's 2022 COVID-19 Omicron waves

The field of software engineering is advancing at astonishing speed, with packages now available to support many stages of data science pipelines. These packages can support infectious disease modelling to be more robust, efficient and transparent, which has been particularly important during the COVID-19 pandemic. We developed a package for the construction of infectious disease models, integrated it with several open-source libraries and applied this composite pipeline to multiple data sources that provided insights into Australia's 2022 COVID-19 epidemic. We aimed to identify the key processes relevant to COVID-19 transmission dynamics and thereby develop a model that could quantify relevant epidemiological parameters.The pipeline's advantages include markedly increased speed, an expressive application programming interface, the transparency of open-source development, easy access to a broad range of calibration and optimisation tools and consideration of the full workflow from input manipulation through to algorithmic generation of the publication materials. Extending the base model to include mobility effects slightly improved model fit to data, with this approach selected as the model configuration for further epidemiological inference. Under our assumption of widespread immunity against severe outcomes from recent vaccination, incorporating an additional effect of the main vaccination programs rolled out during 2022 on transmission did not further improve model fit. Our simulations suggested that one in every two to six COVID-19 episodes were detected, subsequently emerging Omicron subvariants escaped 30–60% of recently acquired natural immunity and that natural immunity lasted only one to eight months on average. We documented our analyses algorithmically and present our methods in conjunction with interactive online code notebooks and plots.We demonstrate the feasibility of integrating a flexible domain-specific syntax library with state-of-the-art packages in high performance computing, calibration, optimisation and visualisation to create an end-to-end pipeline for infectious disease modelling. We used the resulting platform to demonstrate key epidemiological characteristics of the transition from the emergency to the endemic phase of the COVID-19 pandemic.

Epidemiological inference at the threshold of data availability: an influenza A(H1N2)v spillover event in the United Kingdom.

Viruses that infect animals regularly spill over into the human population, but individual events may lead to anything from a single case to a novel pandemic. Rapidly gaining an understanding of a spillover event is critical to calibrating a public health response. We here propose a novel method, using likelihood-free rejection sampling, to evaluate the properties of an outbreak of swine-origin influenza A(H1N2)v in the United Kingdom, detected in November 2023. From the limited data available, we generate historical estimates of the probability that the outbreak had died out in the days following the detection of the first case. Our method suggests that the outbreak could have been said to be over with 95% certainty between 19 and 29 days after the first case was detected, depending upon the probability of a case being detected. We further estimate the number of undetected cases conditional upon the outbreak still being live, the epidemiological parameter R 0, and the date on which the spillover event itself occurred. Our method requires minimal data to be effective. While our calculations were performed after the event, the real-time application of our method has potential value for public health responses to cases of emerging viral infection.

Semiparametric g-computation for survival outcomes with time-fixed exposures: An illustration

PurposeGeneralized (g-) computation is a useful tool for causal inference in epidemiology. However, in settings when the outcome is a survival time subject to right censoring, the standard pooled logistic regression approach to g-computation requires arbitrary discretization of time, parametric modeling of the baseline hazard function, and the need to expand one’s dataset. We illustrate a semiparametric Breslow estimator for g-computation with time-fixed treatments and survival outcomes that is not subject to these limitations. MethodsWe compare performance of the Breslow g-computation estimator to the pooled logistic g-computation estimator in simulations and illustrate both approaches to estimate the effect of a 3-drug vs 2-drug antiretroviral therapy regimen among people with HIV. ResultsIn simulations, both approaches performed well at the end of follow-up. The pooled logistic approach was biased at times between the endpoints of the discrete time intervals used, while the Breslow approach was not. In the example, both approaches estimated a 1-year risk difference of about 6 % in favor of the 3-drug regimen, but the shape of the survival curves differed. ConclusionsThe Breslow g-computation estimator of counterfactual risk functions does not rely on strong parametric assumptions about the time-to-event distribution or onerous dataset expansions.

Causal inference and machine learning in endocrine epidemiology.

With the rapid development of computer science, there is an increasing demand for the use of causal inference methods and machine learning in the research of endocrine disorders and their long-term health outcomes. However, studies on the effective and appropriate applications of these approaches in real-world data and clinical settings are still limited. This review will illustrate the use of causal inference and machine learning in epidemiological research within the field of endocrinology and metabolism. It will examine each concept of causal inference and machine learning through application examples of endocrine disorders. Subsequently, the paper will discuss the integration of machine learning within the causal inference framework, including (i) the estimation of treatment effects or the causal relationship between exposure and outcomes, and (ii) the evaluation of heterogeneity in such treatment effects (or exposure-outcome causal relationship) based on individuals' characteristics. Accurately assessing causal relationships and their heterogeneity across different individuals is crucial not only for determining effective interventions, but also for the appropriate allocation of medical resources and reducing healthcare disparities. By illustrating some application examples in endocrinology, this review aims to enhance readers' understanding and application of causal inference and machine learning in future epidemiological studies focusing on endocrine disorders.

Non-linear associations between meteorological factors, ambient air pollutants and major mosquito-borne diseases in Thailand.

Transmission intensity for mosquito-borne diseases are highly heterogenous and multi-factorial. Understanding risk factors associated to disease transmission allow the optimization of vector control. This study sets out to understand and compare the combined anthropogenic and environmental risk factors of four major mosquito-borne diseases, dengue, malaria, chikungunya and Japanese encephalitis in Thailand. An integrated analysis of mosquito-borne diseases, meteorological and ambient air pollutants of 76 provinces of Thailand was conducted over 2003-2021. We explored the use of generalized linear models and generalized additive models to consider both linear and non-linear associations between meteorological factors, ambient air pollutants and mosquito-borne disease incidence. Different assumptions on spatio-temporal dependence and nonlinearity were considered through province-specific and panel models, as well as different spline functions. Disease-specific model evidence was assessed to select best-fit models for epidemiological inference downstream. Analyses indicated several findings which can be generally applied to all diseases explored: (1) higher AH above mean values was positively associated with disease case counts (2) higher total precipitation above mean values was positively associated with disease case counts (3) extremely high temperatures were negatively associated with disease case counts (4) higher SO2 and PM2.5 surface concentrations were negatively associated with disease case counts. However, the relationships between disease and RH, non-extreme temperatures and CO surface concentration were more mixed, with directions of associations changing across the different diseases considered. This study found protective and enhancing effects of meteorological and ambient air pollutant factors on mosquito-borne diseases burdens in Thailand. Further studies should employ these factors to understand and predict risk factors associated with mosquito-borne disease transmission.

Evaluating low-cost monitoring designs for PM2.5 exposure assessment with a spatiotemporal modeling approach

Determining the most feasible and cost-effective approaches to improving PM2.5 exposure assessment with low-cost monitors (LCMs) can considerably enhance the quality of its epidemiological inferences. We investigated features of fixed-site LCM designs that most impact PM2.5 exposure estimates to be used in long-term epidemiological inference for the Adult Changes in Thought Air Pollution (ACT-AP) study. We used ACT-AP collected and calibrated LCM PM2.5 measurements at the two-week level from April 2017 to September 2020 (N of monitors [measurements] = 82 [502]). We also acquired reference-grade PM2.5 measurements from January 2010 to September 2020 (N = 78 [6186]). We used a spatiotemporal modeling approach to predict PM2.5 exposures with either all LCM measurements or varying subsets with reduced temporal or spatial coverage. We evaluated the models based on a combination of cross-validation and external validation at locations of LCMs included in the models (N = 82), and also based on an independent external validation with a set of LCMs not used for the modeling (N = 30). We found that the model's performance declined substantially when LCM measurements were entirely excluded (spatiotemporal validation R2 [RMSE] = 0.69 [1.2 μg/m3]) compared to the model with all LCM measurements (0.84 [0.9 μg/m3]). Temporally, using the farthest apart measurements (i.e., the first and last) from each LCM resulted in the closest model's performance (0.79 [1.0 μg/m3]) to the model with all LCM data. The models with only the first or last measurement had decreased performance (0.77 [1.1 μg/m3]). Spatially, the model's performance decreased linearly to 0.74 (1.1 μg/m3) when only 10% of LCMs were included. Our analysis also showed that LCMs located in densely populated, road-proximate areas improved the model more than those placed in moderately populated, road-distant areas.

Scientific guidelines for evaluating the validity of forensic feature-comparison methods

When it comes to questions of fact in a legal context—particularly questions about measurement, association, and causality—courts should employ ordinary standards of applied science. Applied sciences generally develop along a path that proceeds from a basic scientific discovery about some natural process to the formation of a theory of how the process works and what causes it to fail, to the development of an invention intended to assess, repair, or improve the process, to the specification of predictions of the instrument’s actions and, finally, empirical validation to determine that the instrument achieves the intended effect. These elements are salient and deeply embedded in the cultures of the applied sciences of medicine and engineering, both of which primarily grew from basic sciences. However, the inventions that underlie most forensic science disciplines have few roots in basic science, and they do not have sound theories to justify their predicted actions or results of empirical tests to prove that they work as advertised. Inspired by the “Bradford Hill Guidelines”—the dominant framework for causal inference in epidemiology—we set forth four guidelines that can be used to establish the validity of forensic comparison methods generally. This framework is not intended as a checklist establishing a threshold of minimum validity, as no magic formula determines when particular disciplines or hypotheses have passed a necessary threshold. We illustrate how these guidelines can be applied by considering the discipline of firearm and tool mark examination.

Environmental transmission of influenza A virus in mallards.

Wild birds are the natural reservoir hosts of influenza A viruses. Highly pathogenic strains of influenza A viruses pose risks to wild birds, poultry, and human health. Thus, understanding how these viruses are transmitted between birds is critical. We conducted an experiment where we experimentally infected mallards which are ducks that are commonly exposed to influenza viruses. We exposed several contact ducks to the experimentally infected duck to estimate the probability that a contact duck would become infected from either exposure to the virus shed directly from the infected duck or shared water contaminated with the virus from the infected duck. We found that environmental transmission from contaminated water best predicted the probability of transmission to naïve contact ducks, relatively low levels of virus in the water were sufficient to cause infection, and the probability of a naïve duck becoming infected varied over time.

Data quality control in longitudinal epidemiologic studies: conditional studentized residuals from linear mixed effects models for outlier detection in the setting of pediatric chronic kidney disease

PurposeQuality control in longitudinal cohort studies is critical for valid epidemiologic inference. Conditional studentized residuals (CSRs) derived from linear mixed effects models offer efficient individual-specific quality control. We present the utility of CSRs for outlier detection in an applied example using data from the Chronic Kidney Disease in Children cohort. MethodsLongitudinal linear mixed effects models with glomerular filtration rate (GFR) as the outcome were fit for observations prior to kidney replacement therapy, stratified by nonglomerular or glomerular diagnosis, and for a subset after receiving a kidney transplant. For each model, CSRs were calculated and values ≥±5 were considered potential outliers for further investigation. ResultsA total of 1096 participants contributed 6881 annual measures of GFR across the two diagnostic groups and after transplant. In all models, the fixed effects captured progressive GFR decline. CSRs provided measures of individual-level deviations from the modeled trajectories (random + fixed effects) and were easily visualized in longitudinal plots. A total of 38 potential outliers from 32 participants were detected and further investigated for quality control. ConclusionsThis example demonstrated how longitudinal models can provide CSRs to detect individual-specific outliers. CSRs should be considered as part of quality control for longitudinal epidemiologic studies.

Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods

AbstractAddressing the challenge of scaling-up epidemiological inference to complex and heterogeneous models, we introduce Poisson approximate likelihood (PAL) methods. In contrast to the popular ordinary differential equation (ODE) approach to compartmental modelling, in which a large population limit is used to motivate a deterministic model, PALs are derived from approximate filtering equations for finite-population, stochastic compartmental models, and the large population limit drives consistency of maximum PAL estimators. Our theoretical results appear to be the first likelihood-based parameter estimation consistency results which apply to a broad class of partially observed stochastic compartmental models and address the large population limit. PALs are simple to implement, involving only elementary arithmetic operations and no tuning parameters, and fast to evaluate, requiring no simulation from the model and having computational cost independent of population size. Through examples we demonstrate how PALs can be used to: fit an age-structured model of influenza, taking advantage of automatic differentiation in Stan; compare over-dispersion mechanisms in a model of rotavirus by embedding PALs within sequential Monte Carlo; and evaluate the role of unit-specific parameters in a meta-population model of measles.

Genomic epidemiology of Mycobacterium bovis infection in sympatric badger and cattle populations in Northern Ireland.

Bovine tuberculosis (bTB) is a costly, epidemiologically complex, multi-host, endemic disease. Lack of understanding of transmission dynamics may undermine eradication efforts. Pathogen whole-genome sequencing improves epidemiological inferences, providing a means to determine the relative importance of inter- and intra-species host transmission for disease persistence. We sequenced an exceptional data set of 619 Mycobacterium bovis isolates from badgers and cattle in a 100 km2 bTB 'hotspot' in Northern Ireland. Historical molecular subtyping data permitted the targeting of an endemic pathogen lineage, whose long-term persistence provided a unique opportunity to study disease transmission dynamics in unparalleled detail. Additionally, to assess whether badger population genetic structure was associated with the spatial distribution of pathogen genetic diversity, we microsatellite genotyped hair samples from 769 badgers trapped in this area. Birth death models and TransPhylo analyses indicated that cattle were likely driving the local epidemic, with transmission from cattle to badgers being more common than badger to cattle. Furthermore, the presence of significant badger population genetic structure in the landscape was not associated with the spatial distribution of M. bovis genetic diversity, suggesting that badger-to-badger transmission is not playing a major role in transmission dynamics. Our data were consistent with badgers playing a smaller role in transmission of M. bovis infection in this study site, compared to cattle. We hypothesize, however, that this minor role may still be important for persistence. Comparison to other areas suggests that M. bovis transmission dynamics are likely to be context dependent, with the role of wildlife being difficult to generalize.

Unraveling the epidemiology of Mycobacterium bovis using whole-genome sequencing combined with environmental and demographic data.

When studying the dynamics of a pathogen in a host population, one crucial question is whether it transitioned from an epidemic (i.e., the pathogen population and the number of infected hosts are increasing) to an endemic stable state (i.e., the pathogen population reached an equilibrium). For slow-growing and slow-evolving clonal pathogens such as Mycobacterium bovis, the causative agent of bovine (or animal) and zoonotic tuberculosis, it can be challenging to discriminate between these two states. This is a result of the combination of suboptimal detection tests so that the actual extent of the pathogen prevalence is often unknown, as well as of the low genetic diversity, which can hide the temporal signal provided by the accumulation of mutations in the bacterial DNA. In recent years, the increased availability, efficiency, and reliability of genomic reading techniques, such as whole-genome sequencing (WGS), have significantly increased the amount of information we can use to study infectious diseases, and therefore, it has improved the precision of epidemiological inferences for pathogens such as M. bovis. In this study, we use WGS to gain insights into the epidemiology of M. bovis in Cameroon, a developing country where the pathogen has been reported for decades. A total of 91 high-quality sequences were obtained from tissue samples collected in four abattoirs, 64 of which were with complete metadata. We combined these with environmental, demographic, ecological, and cattle movement data to generate inferences using phylodynamic models. Our findings suggest M. bovis in Cameroon is slowly expanding its epidemiological range over time; therefore, endemic stability is unlikely. This suggests that animal movement plays an important role in transmission. The simultaneous prevalence of M. bovis in co-located cattle and humans highlights the risk of such transmission being zoonotic. Therefore, using genomic tools as part of surveillance would vastly improve our understanding of disease ecology and control strategies.

Invited Perspective: Still Hazy? Air Pollution and Acute Kidney Injury.

Invited Perspective: Still Hazy? Air Pollution and Acute Kidney Injury.

Molecular Capture of Mycobacterium tuberculosis Genomes Directly from Clinical Samples: A Potential Backup Approach for Epidemiological and Drug Susceptibility Inferences.

The application of whole genome sequencing of Mycobacterium tuberculosis directly on clinical samples has been investigated as a means to avoid the time-consuming need for culture isolation that can lead to a potential prolonged suboptimal antibiotic treatment. We aimed to provide a proof-of-concept regarding the application of the molecular capture of M. tuberculosis genomes directly from positive sputum samples as an approach for epidemiological and drug susceptibility predictions. Smear-positive sputum samples (n = 100) were subjected to the SureSelectXT HS Target Enrichment protocol (Agilent Technologies, Santa Clara, CA, USA) and whole-genome sequencing analysis. A higher number of reads on target were obtained for higher smear grades samples (i.e., 3+ followed by 2+). Moreover, 37 out of 100 samples showed ≥90% of the reference genome covered with at least 10-fold depth of coverage (27, 9, and 1 samples were 3+, 2+, and 1+, respectively). Regarding drug-resistance/susceptibility prediction, for 42 samples, ≥90% of the >9000 hits that are surveyed by TB-profiler were detected. Our results demonstrated that M. tuberculosis genome capture and sequencing directly from clinical samples constitute a potential valid backup approach for phylogenetic inferences and resistance prediction, essentially in settings when culture is not routinely performed or for samples that fail to grow.

Inference from Open-Source Sequence Data on the Genetic Epidemiology of COVID-19 Infection in Africa

Between late December 2019 when it was first reported, to early September 2020, severe acute respiratory syndrome coronavirus-2 (SARSCoV- 2), the causative agent of coronavirus disease-2019 (COVID-19), reportedly infected over 10 million people globally. In Africa, more than 300,000 infections occurred within the period, from which several genetic sequences of the virus were generated. During disease outbreak, phylogenetic reconstruction of genomic data can assist in making epidemiological inferences about time of pathogen introduction, epidemic growth rate and temporal-spatial spread of the infection. In this work, we studied the genetic epidemiology of COVID-19 in Africa. Genetic sequence data of SARS-CoV-2 and metadata from African countries were obtained from publicly accessible sequence database hosted by the GISAID initiative. Whole genome sequences were subjected to multiple sequence alignment and the aligned sequences used to construct a Maximum Likelihood phylogenetic tree based on the general time reversible model. Of the 227 genetic sequences obtained for 9 African countries (DRC=133, Senegal=23, South Africa=20, Ghana=15, Tunisia=6, Algeria=3, Gambia=3, Egypt=2 and Nigeria=2), 220 were whole genome sequences while 7 were partial genome sequences of the surface glycoprotein S. Majority of the viruses (58.1%) belonged to the G clade of GISAID classification. Phylogenetic analysis confirmed multiple introductions of the virus to the continent from multiple external sources prior to local adaptation and spread. The very close alignment of three viruses - Ghana/1659_S14/2020|EPI_ISL_422405, DRC/KN0054/2020|EPI_ ISL_417437, and South_Africa/R05475/2020|EPI_ISL_435059 – to the reference Wuhan strain on the time tree, suggests possible introduction and circulation of the virus into the continent much earlier than when the first case in the continent was announced in February 15 2020. In conclusion, this study provided evidence to support multiple introductions of SARSCoV- 2 into Africa, and further suggests that the virus may have already been circulating in the continent prior to official reporting of the first case. Also, there is strong impression to infer likely genetic adaptation of the virus in the continent that may account for the close clustering of isolates from different countries of the continent.

Detecting Leishmania in dogs: A hierarchical-modeling approach to investigate the performance of parasitological and qPCR-based diagnostic procedures.

Domestic dogs are primary reservoir hosts of Leishmania infantum, the agent of visceral leishmaniasis. Detecting dog infections is central to epidemiological inference, disease prevention, and veterinary practice. Error-free diagnostic procedures, however, are lacking, and the performance of those available is difficult to measure in the absence of fail-safe "reference standards". Here, we illustrate how a hierarchical-modeling approach can be used to formally account for false-negative and false-positive results when investigating the process of Leishmania detection in dogs. We studied 294 field-sampled dogs of unknown infection status from a Leishmania-endemic region. We ran 350 parasitological tests (bone-marrow microscopy and culture) and 1,016 qPCR assays (blood, bone-marrow, and eye-swab samples with amplifiable DNA). Using replicate test results and site-occupancy models, we estimated (a) clinical sensitivity for each diagnostic procedure and (b) clinical specificity for qPCRs; parasitological tests were assumed 100% specific. Initial modeling revealed qPCR specificity < 94%; we tracked the source of this unexpected result to some qPCR plates having subtle signs of possible contamination. Using multi-model inference, we formally accounted for suspected plate contamination and estimated qPCR sensitivity at 49-53% across sample types and dog clinical conditions; qPCR specificity was high (95-96%), but fell to 81-82% for assays run in plates with suspected contamination. The sensitivity of parasitological procedures was low (~12-13%), but increased to ~33% (with substantial uncertainty) for bone-marrow culture in seriously-diseased dogs. Leishmania-infection frequency estimates (~49-50% across clinical conditions) were lower than observed (~60%). We provide statistical estimates of key performance parameters for five diagnostic procedures used to detect Leishmania in dogs. Low clinical sensitivies likely reflect the absence of Leishmania parasites/DNA in perhaps ~50-70% of samples drawn from infected dogs. Although qPCR performance was similar across sample types, non-invasive eye-swabs were overall less likely to contain amplifiable DNA. Finally, modeling was instrumental to discovering (and formally accounting for) possible qPCR-plate contamination; even with stringent negative/blank-control scoring, ~4-5% of positive qPCRs were most likely false-positives. This work shows, in sum, how hierarchical site-occupancy models can sharpen our understanding of the problem of diagnosing host infections with hard-to-detect pathogens including Leishmania.

Modeling the spatial distribution of COVID-19 infections in Europe reveals no similarities between countries during the first year of the pandemic

We considered the spatial (dis)similarities in the number of SARS-CoV-2 infected cases across Europe, through the first 300 days of the pandemic. The data represents the SARS-CoV-2 Repository provided by the Center for Systems Science and Engineering at Johns Hopkins University. From this data set, we extracted daily numbers of infected cases per million inhabitants for 33 European countries between February and November 2020. Spatial clustering was applied to define groups of countries with similar infection patterns. Two clusters of countries comprised: 10 low-incidence countries and 23 ‘remaining’ countries. Moran indices estimated for all countries, as well as separately within each cluster, every 10 days, did not significantly deviate from zero. Reasons for the lack of spatial correlations comprise differences in the regulation of social contacts, demographic patterns, public health facilities, and varying accuracy of data recording. The major limitation of the analysis is data quality, which hampers epidemiological inferences.

Specificity of association in epidemiology

The epidemiologist Bradford Hill famously argued that in epidemiology, specificity of association (roughly, the fact that an environmental or behavioral risk factor is associated with just one or at most a few medical outcomes) is strong evidence of causation. Prominent epidemiologists have dismissed Hill’s claim on the ground that it relies on a dubious `one-cause one effect’ model of disease causation. The paper examines this methodological controversy, and argues that specificity considerations do have a useful role to play in causal inference in epidemiology. More precisely, I argue that specificity considerations help solve a pervasive inferential problem in contemporary epidemiology: the problem of determining whether an exposure-outcome correlation might be due to confounding by a social factor. This examination of specificity has interesting consequences for our understanding of the methodology of epidemiology. It highlights how the methodology of epidemiology relies on local tools designed to address specific inference problems peculiar to the discipline, and shows that observational causal inference in epidemiology can proceed with little prior knowledge of the causal structure of the phenomenon investigated. I also argue that specificity of association cannot (despite claims to the contrary) be entirely explained in terms of Woodward’s well-known concept of “one-to-one” causal specificity. This is because specificity as understood by epidemiologists depends on whether an exposure (or outcome) is associated with a `heterogeneous’ set of variables. This dimension of heterogeneity is not captured in Woodward’s notion, but is crucial for understanding the evidential import of specificity of association.

A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data.

Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor SARS-CoV-2 transmission. However, epidemiological inference from WBS data remains understudied and limits its application. In this study, we have established a quantitative framework to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission through integrating WBS data into an SEIR-V model. We conceptually divide the individual-level viral shedding course into exposed, infectious, and recovery phases as an analogy to the compartments in a population-level SEIR model. We demonstrated that the effect of temperature on viral losses in the sewer can be straightforwardly incorporated in our framework. Using WBS data from the second wave of the pandemic (Oct 02, 2020–Jan 25, 2021) in the Greater Boston area, we showed that the SEIR-V model successfully recapitulates the temporal dynamics of viral load in wastewater and predicts the true number of cases peaked earlier and higher than the number of reported cases by 6–16 days and 8.3–10.2 folds (R = 0.93). This work showcases a simple yet effective method to bridge WBS and quantitative epidemiological modeling to estimate the prevalence and transmission of SARS-CoV-2 in the sewershed, which could facilitate the application of wastewater surveillance of infectious diseases for epidemiological inference and inform public health actions.

Ralstonia Strains from Potato-Growing Regions of Kenya Reveal Two Phylotypes and Epidemic Clonality of Phylotype II Sequevar 1 Strains.

Bacterial wilt, caused by the Ralstonia solanacearum species complex (RSSC), is the most destructive potato disease in Kenya. Studies were conducted to (i) determine the molecular diversity of RSSC strains associated with bacterial wilt of potato in Kenya, (ii) generate an RSSC distribution map for epidemiological inference, and (iii) determine whether phylotype II sequevar 1 strains exhibit epidemic clonality. Surveys were conducted in 2018 and 2019, in which tubers from wilting potato plants and stem samples of potential alternative hosts were collected for pathogen isolation. The pathogen was phylotyped by multiplex PCR and 536 RSSC strains typed at a sequevar level. Two RSSC phylotypes were identified, phylotype II (98.4%, n = 506 [sequevar 1 (n = 505) and sequevar 2 (n = 1)]) and phylotype I (1.6%, n = 30 [sequevar 13 (n = 9) and a new sequevar (n = 21)]). The phylotype II sequevar 1 strains were haplotyped using multilocus tandem repeat sequence typing (TRST) schemes. The TRST scheme identified 51 TRST profiles within the phylotype II sequevar 1 strains with a modest diversity index (HGDI = 0.87), confirming the epidemic clonality of RSSC phylotype II sequevar 1 strains in Kenya. A minimum spanning tree and mapping of the TRST profiles revealed that TRST27 '8-5-12-7-5' is the primary founder of the clonal complex of RSSC phylotype II sequevar 1 and is widely distributed via latently infected seed tubers. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.