Latent class analysis identifies multimorbidity patterns in pigs with respiratory disease.

Respiratory disease is one of the major causes of losses to the pig industry worldwide. The pig subsector is the largest component of the livestock sector in the Philippines. Using lung scoring, this study aimed to estimate the prevalence of thoracic lesions in slaughter-age pigs in two provinces in the Philippines (Batangas and Albay) and define classes for respiratory health of pigs characterised by different patterns of thoracic lesions. A total of 260 pigs from Batangas and 300 pigs from Albay from either commercial or backyard farm types were included in this cross-sectional study. Lungs were scored for cranio-ventral pneumonia (0-55) and pleurisy (0-3). Presence or absence of pericarditis as well as focal dorso-caudal pneumonia were recorded. Latent class analyses considering four indicator variables, and province and farm type as covariates were used to explore different patterns of thoracic lesions across the study populations. Using a threshold of ≥7, the prevalence of a high lung score was 51.9% (95% confidence interval [CI]: 42.3-61.4%) and 13.7% (95% CI: 8.1-22.2%) in Batangas and Albay, respectively. Similarly, the prevalence of a pleurisy score of ≥1 was 56.9% (95% CI: 37.5-74.4%) and 5.0% (95% CI: 2.9-8.4%), pericarditis 24.6% (95%CI: 10.1-48.6%) and 1.7% (95%CI: 0.3-6.7%) and focal dorso-caudal pneumonia lesions 7.7% (95% CI: 3.7-15.5%) and 0% (97.5% one-sided CI: 0-1.2%), respectively. Latent class analyses identified four classes based on lung score, pleurisy score and the presence/absence of pericarditis: "healthy", "mild respiratory disease", "moderate pneumonia", and "multi-lesion". The relative frequency of these classes differed with province and farm type. Most pigs from Albay were "healthy", whereas in Batangas most pigs from commercial farms were "multi-lesion" and those from backyard farms were in the "mild respiratory disease" class. This study has provided baseline data on thoracic lesions in slaughter-age pigs for the provinces of Batangas and Albay in the Philippines. Targeting farms and areas where "multi-lesion pigs" are most common and further research to identify risk factors for particular classes should maximize impact of future control measures. The latent class analysis approach used could be applied more widely and could add value to analysis of multi-morbidity data collected routinely as part of ongoing monitoring schemes.

Rapid in-country sequencing of whole virus genomes to inform rabies elimination programmes.

Genomic surveillance is an important aspect of contemporary disease management but has yet to be used routinely to monitor endemic disease transmission and control in low- and middle-income countries. Rabies is an almost invariably fatal viral disease that causes a large public health and economic burden in Asia and Africa, despite being entirely vaccine preventable. With policy efforts now directed towards achieving a global goal of zero dog-mediated human rabies deaths by 2030, establishing effective surveillance tools is critical. Genomic data can provide important and unique insights into rabies spread and persistence that can direct control efforts. However, capacity for genomic research in low- and middle-income countries is held back by limited laboratory infrastructure, cost, supply chains and other logistical challenges. Here we present and validate an end-to-end workflow to facilitate affordable whole genome sequencing for rabies surveillance utilising nanopore technology. We used this workflow in Kenya, Tanzania and the Philippines to generate rabies virus genomes in two to three days, reducing costs to approximately £60 per genome. This is over half the cost of metagenomic sequencing previously conducted for Tanzanian samples, which involved exporting samples to the UK and a three- to six-month lag time. Ongoing optimization of workflows are likely to reduce these costs further. We also present tools to support routine whole genome sequencing and interpretation for genomic surveillance. Moreover, combined with training workshops to empower scientists in-country, we show that local sequencing capacity can be readily established and sustainable, negating the common misperception that cutting-edge genomic research can only be conducted in high resource laboratories. More generally, we argue that the capacity to harness genomic data is a game-changer for endemic disease surveillance and should precipitate a new wave of researchers from low- and middle-income countries.