Many microbial functions happen within communities of interacting species. Explaining how species with disparate growth rates can coexist is important for applications such as manipulating host-associated microbiota or engineering industrial communities. Here, we ask how microbes interacting through their chemical environment can achieve coexistence in a continuous growth setup (similar to an industrial bioreactor or gut microbiota) where external resources are being supplied. We formulate and experimentally constrain a model in which mediators of interactions (e.g. metabolites or waste-products) are explicitly incorporated. Our model highlights facilitation and self-restraint as interactions that contribute to coexistence, consistent with our intuition. When interactions are strong, we observe that coexistence is determined primarily by the topology of facilitation and inhibition influences not their strengths. Importantly, we show that consumption or degradation of chemical mediators moderates interaction strengths and promotes coexistence. Our results offer insights into how to build or restructure microbial communities of interest.

Many microbial functions happen within communities of interacting species. Explaining how species with intrinsically disparate fitness can coexist is important for applications such as manipulating hostassociated microbiota or engineering industrial communities. Previous coexistence studies have often neglected interaction mechanisms. Here, we formulate and experimentally constrain a model in which chemical mediators of microbial interactions (e.g. metabolites or waste-products) are explicitly incorporated. We construct many instances of coexistence by simulating community assembly through enrichment and ask how species interactions can explain coexistence. We show that growth-facilitating influences between members are favored in assembled communities. Among negative influences, selfrestraint, such as production of self-inhibiting waste, contributes to coexistence, whereas inhibition of other species disrupts coexistence. Coexistence is also favored when interactions are mediated by depletable chemicals that get consumed or degraded, rather than by reusable chemicals that are unaffected by recipients. Our model creates null predictions for coexistence driven by chemicalmediated interactions.

Background Since the emergence of the COVID-19 pandemic, substantial concern has surrounded its impact among the Rohingya refugees living in the Kutupalong-Balukhali refugee camps in Bangladesh. Early modeling work projected a massive outbreak was likely after an introduction of the SARS-CoV-2 virus into the camps. Despite this, only 317 laboratory-confirmed cases and 10 deaths were reported through October 2020. While these official numbers portray a situation where the virus has been largely controlled, other sources contradict this, suggesting the low reported numbers to be a result of limited care seeking and testing, highlighting a population not willing to seek care or be tested. SARS-CoV-2 seroprevalence estimates from similar a timeframe in India (57%) and Bangladesh (74%) further sow doubt that transmission had been controlled. Here we explore multiple data sources to understand the plausibility of a much larger SARS-CoV-2 outbreak among the Rohingya refugees. Methods We used a mixed approach to analyze SARS-CoV-2 transmission using multiple available datasets. Using data from reported testing, cases, and deaths from the World Health Organization (WHO) and from WHO's Emergency Warning, Alert, and Response System, we characterized the probabilities of care seeking, testing, and being positive if tested. Unofficial death data, including reported pre-death symptoms, come from a community-based mortality survey conducted by the International Organization for Migration (IOM),) in addition to community health worker reported deaths. We developed a probabilistic inference framework, drawing on these data sources, to explore three scenarios of what might have happened among the Rohingya refugees. Results Among the 144 survey-identified deaths, 48 were consistent with suspected COVID-19. These deaths were consistent with viral exposures during Ramadan, a period of increased social contacts, and coincided with a spike in reported cases and testing positivity in June 2020. The age profile of suspected COVID-19 deaths mirrored that expected. Through the probability framework, we find that under each scenario, a substantial outbreak likely occurred, though the cumulative size and timing vary considerably. In conjunction with the reported and suspected deaths, the data suggest a large outbreak could have occurred early during spring 2020. Furthermore, while many mild and asymptomatic infections likely occurred, death data analyzed suggest there may have been significant unreported mortality. Conclusions With the high population density, inability to home isolate adequately, and limited personal protective equipment, infection prevention and control in the Rohingya population is extremely challenging. Despite the low reported numbers of cases and deaths, our results suggest an early large-scale outbreak is consistent with multiple sources of data, particularly when accounting for limited care seeking behavior and low infection severity among this young population. While the currently available data do not allow us to estimate the precise incidence, these results indicate substantial unrecognized SARS-CoV-2 transmission may have occurred in these camps. However, until serological testing provides more conclusive evidence, we are only able to speculate about the extent of transmission among the Rohingya.

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