ABSTRACT
Background Co-circulating respiratory pathogens can interfere with or promote each other, leading to important effects on disease epidemiology. Estimating the magnitude of pathogen-pathogen interactions from clinical specimens is challenging because sampling from symptomatic individuals can create biased estimates. Methods We conducted an observational, cross-sectional study using samples collected by the Seattle Flu Study between 11 November 2018 and 20 August 2021. Samples that tested positive via RT-qPCR for at least one of 17 potential respiratory pathogens were included in this study. Semi-quantitative cycle threshold (Ct) values were used to measure pathogen load. Differences in pathogen load between monoinfected and coinfected samples were assessed using linear regression adjusting for age, season, and recruitment channel. Results 21,686 samples were positive for at least one potential pathogen. Most prevalent were rhinovirus (33·5%), Streptococcus pneumoniae ( SPn , 29·0%), SARS-CoV-2 (13.8%) and influenza A/H1N1 (9·6%). 140 potential pathogen pairs were included for analysis, and 56 (40%) pairs yielded significant Ct differences (p < 0.01) between monoinfected and co-infected samples. We observed no virus-virus pairs showing evidence of significant facilitating interactions, and found significant viral load decrease among 37 of 108 (34%) assessed pairs. Samples positive with SPn and a virus were consistently associated with increased SPn load. Conclusions Viral load data can be used to overcome sampling bias in studies of pathogen-pathogen interactions. When applied to respiratory pathogens, we found evidence of viral- SPn facilitation and several examples of viral-viral interference. Multipathogen surveillance is a cost-efficient data collection approach, with added clinical and epidemiological informational value over single-pathogen testing, but requires careful analysis to mitigate selection bias.
Subject(s)
Influenza, Human , Pneumococcal InfectionsABSTRACT
Background: Long-term suppression of SARS-CoV-2 transmission will require context-specific strategies that recognize the heterogeneous capacity of communities to undertake public health recommendations, particularly due to limited access to food, sanitation facilities, and physical space required for self-quarantine or isolation. We highlight the epidemiological impact of barriers to adoption of public health recommendations by urban slum populations in low- and middle-income countries (LMICs) and the potential role of community-based initiatives to coordinate efforts that support cases and high-risk contacts. Methods: Daily case updates published by the National Public Health Institute of Liberia were used to inform a stratified stochastic compartmental model representing transmission of SARS-CoV-2 in two subpopulations (urban poor versus less socioeconomically vulnerable) of Montserrado County, Liberia. Differential transmission was considered at levels of the subpopulation, household versus community, and events (i.e., funerals). Adoption of home-isolation behavior was assumed to be related to the proportion of each subpopulation residing in housing units with multiple rooms, access to sanitation facilities, and access to basic goods like water and food. Percentage reductions in cumulative infection counts, cumulative counts of severe cases, and maximum daily infection counts for each subpopulation were evaluated across intervention scenarios that included symptom-triggered, community-driven efforts to support high-risk contacts and confirmed cases in self-isolation following the scheduled lifting of the state of emergency. Results: Modeled outbreaks for the status quo scenario differed between the two subpopulations, with increased overall infection burden but decreased numbers of severe cases in the urban poor subpopulation relative to the less socioeconomically vulnerable population after 180 days post-introduction into Liberia. With more proactive self-isolation by mildly symptomatic individuals after lifting of the public health emergency, median reductions in cumulative infections, severe cases, and maximum daily incidence were 7.6% (IQR: 2.2%-20.9%), 7.0% (2.0%-18.5%), and 9.9% (2.5%-31.4%) for cumulative infections, severe cases, and maximum daily incidence, respectively, across epidemiological curve simulations in the urban poor subpopulation and 16.8% (5.5%-29.3%), 15.0% (5.0%-26.4%), and 28.1% (IQR: 9.3%-47.8%) in the less socioeconomically vulnerable population. An increase in the maximum attainable percentage of behavior adoption by the urban slum subpopulation, with the provision of support to facilitate self-isolation or quarantine, was associated with median reductions in cumulative infections, severe cases, and maximum daily incidence were 19.2% (IQR: 10.1%-34.0%), 21.1% (IQR: 13.3%-34.2%), and 26.0% (IQR: 11.5%-48.9%), respectively, relative to the status quo scenario. Conclusions: Broadly supported post-lockdown recommendations that prioritize proactively monitoring symptoms, seeking testing and isolating at home by confirmed cases are limited by resource constraints in urban poor communities. Investing in community-based initiatives that determine needs and coordinate needs-based support for self-identified cases and their contacts could provide a more effective, longer-term strategy for suppressing transmission of COVID-19 in settings with prevalent distrust and socioeconomic vulnerabilities.