Characterizing mobility patterns and malaria risk factors in semi-nomadic populations of Northern Kenya.

While many studies have characterized mobility patterns and disease dynamics of settled populations, few have focused on more mobile populations. Highly mobile groups are often at higher disease risk due to their regular movement that may increase the variability of their environments, reduce their access to health care, and limit the number of intervention strategies suitable for their lifestyles. Quantifying the movements and their associated disease risks will be key to developing interventions more suitable for mobile populations. Turkana, Kenya is an ideal setting to characterize these relationships. While the vast, semi-arid county has a large mobile population (>60%) and was recently shown to have endemic malaria, the relationship between mobility and malaria risk in this region has not yet been defined. Here, we worked with 250 semi-nomadic households from four communities in Central Turkana to 1) characterize mobility patterns of travelers and 2) test the hypothesis that semi-nomadic individuals are at greater risk of malaria exposure when migrating with their herds than when staying at their semi-permanent settlements. Participants provided medical and travel histories, demographics, and a dried blood spot for malaria testing before and after the travel period. Further, a subset of travelers was given GPS loggers to document their routes. Four travel patterns emerged from the logger data, Long Term, Transient, Day trip, and Static, with only Long Term and Transient trips being associated with malaria cases detected in individuals who carried GPS devices. After completing their trips, travelers had a higher prevalence of malaria than those who remained at the household (9.2% vs 4.4%), regardless of gender and age. These findings highlight the need to develop intervention strategies amenable to mobile lifestyles that can ultimately help prevent the transmission of malaria.

Characterizing mobility patterns and malaria risk factors in semi-nomadic populations of Northern Kenya.

While many studies have characterized mobility patterns and disease dynamics of individuals from settled populations, few have focused on more mobile populations. Highly mobile groups are often at higher disease risk due to their regular movement that may increase the variability of their environments, reduce their access to health care, and limit the number of intervention strategies suitable for their lifestyles. Quantifying the movements and their associated disease risks will be key to developing intervention strategies more suitable for mobile populations. Here, we worked with four semi-nomadic communities in Central Turkana, Kenya to 1) characterize mobility patterns of travelers from semi-nomadic communities and 2) test the hypothesis that semi-nomadic individuals are at greater risk of exposure to malaria during seasonal migrations than when staying at their semi-permanent settlements. From March-October, 2021, we conducted a study in semi-nomadic households (n=250) where some members traveled with their herd while others remained at the semi-permanent settlement. Participants provided medical and travel histories, demographics, and a dried blood spot for malaria testing before and after the travel period. Further, a subset of travelers was given GPS loggers to document their routes. Four travel patterns emerged from the logger data, Long Term, Transient, Day trip, and Static, with only Long Term and Transient trips being associated with malaria cases detected in individuals who carried GPS devices. After completing their trips, travelers had a higher prevalence of malaria than those who remained at the household (9.2% vs 4.4%), regardless of gender, age group, and catchment area. These findings highlight the need to develop intervention strategies amenable to mobile lifestyles that can ultimately help prevent the transmission of malaria.

Plasmodium falciparum importation does not sustain malaria transmission in a semi-arid region of Kenya.

Human movement impacts the spread and transmission of infectious diseases. Recently, a large reservoir of Plasmodium falciparum malaria was identified in a semi-arid region of northwestern Kenya historically considered unsuitable for malaria transmission. Understanding the sources and patterns of transmission attributable to human movement would aid in designing and targeting interventions to decrease the unexpectedly high malaria burden in the region. Toward this goal, polymorphic parasite genes (ama1, csp) in residents and passengers traveling to Central Turkana were genotyped by amplicon deep sequencing. Genotyping and epidemiological data were combined to assess parasite importation. The contribution of travel to malaria transmission was estimated by modelling case reproductive numbers inclusive and exclusive of travelers. P. falciparum was detected in 6.7% (127/1891) of inbound passengers, including new haplotypes which were later detected in locally-transmitted infections. Case reproductive numbers approximated 1 and did not change when travelers were removed from transmission networks, suggesting that transmission is not fueled by travel to the region but locally endemic. Thus, malaria is not only prevalent in Central Turkana but also sustained by local transmission. As such, interrupting importation is unlikely to be an effective malaria control strategy on its own, but targeting interventions locally has the potential to drive down transmission.

Characterizing human mobility patterns in rural settings of sub-Saharan Africa.

Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.

Epidemiology of Plasmodium falciparum Infections in a Semi-Arid Rural African Setting: Evidence from Reactive Case Detection in Northwestern Kenya.

In northwestern Kenya, Turkana County has been historically considered unsuitable for stable malaria transmission because of its unfavorable climate and predominantly semi-nomadic population; consequently, it is overlooked during malaria control planning. However, the area is changing, with substantial development, an upsurge in travel associated with resource extraction, and more populated settlements forming. Recently, numerous malaria outbreaks have highlighted the need to characterize malaria transmission and its associated risk factors in the region to inform control strategies. Reactive case detection of confirmed malaria cases at six health facilities across central Turkana was conducted from 2018 to 2019. Infections in household members of index cases were detected by malaria rapid diagnostic tests (RDTs) and PCR tests, and they were grouped according household and individual characteristics. The relationships between putative risk factors and infection were quantified by multilevel logistic regression models. Of the 3,189 household members analyzed, 33.6% had positive RDT results and/or PCR test results. RDT-detected infections were more prevalent in children; however, PCR-detected infections were similarly prevalent across age groups. Recent travel was rarely reported and not significantly associated with infection. Bed net coverage was low and net crowding was associated with increased risks of household infections. Infections were present year-round, and fluctuations in prevalence were not associated with rainfall. These findings indicate year-round, endemic transmission with moderate population immunity. This is in stark contrast to recent estimates in this area. Therefore, further investigations to design effective intervention approaches to address malaria in this rapidly changing region and other similar settings across the Horn of Africa are warranted.

Colonization with multidrug-resistant Enterobacteriaceae among infants: an observational study in southern Sri Lanka.

BACKGROUND: The timing of and risk factors for intestinal colonization with multidrug-resistant Enterobacteriaceae (MDRE) are still poorly understood in areas with high MDRE carriage. We determined the prevalence, timing, and risk factors associated with MDRE intestinal colonization among infants in southern Sri Lanka. METHODS: Women and their newborn children were enrolled within 48 h after delivery in southern Sri Lanka. Rectal swabs were collected from women and infants at enrollment and 4-6 weeks later. Enterobacteriaceae were isolated and identified as MDRE (positive for extended-spectrum ß-lactamases or carbapenem resistant) using standard microbiologic procedures. We used exact methods (Fisher's exact and Kruskal-Wallis tests) and multivariable logistic regression to identify sociodemographic and clinical features associated with MDRE intestinal colonization. Whole-genome sequencing was performed on selected MDRE isolates to identify phylogroups and antibiotic resistance-encoding genes were identified with NCBI's AMRfinder tool. RESULTS: Overall, 199 post-partum women and 199 infants were enrolled; 148/199 (74.4%) women and 151/199 (75.9%) infants were reassessed later in the community. Twenty-four/199 (12.1%) women and 3/199 (1.5%) infants displayed intestinal colonization with MDRE at enrollment, while 26/148 (17.6%) women and 24/151 (15.9%) infants displayed intestinal colonization with MDRE at the reassessment. While there were no risk factors associated with infant colonization at enrollment, multivariable analysis indicated that risk factors for infant colonization at reassessment included mother colonized at enrollment (aOR = 3.62) or reassessment (aOR = 4.44), delivery by Cesarean section (aOR = 2.91), and low birth weight (aOR = 5.39). Of the 20 MDRE isolates from infants that were sequenced, multilocus sequence typing revealed that 6/20 (30%) were clustered on the same branch as MDRE isolates found in the respective mothers. All sequenced isolates for mothers (47) and infants (20) had at least one ESBL-producing gene. Genes encoding fosfomycin resistance were found in 33/47 (70%) of mothers' isolates and 16/20 (80%) of infants' isolates and genes encoding resistance to colistin were found in one (2%) mother's isolate. CONCLUSIONS: Our results suggest that a substantial proportion of infants undergo MDRE intestinal colonization within 6 weeks of birth, potentially due to postnatal rather than intranatal transmission.

Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios - United States, April-September 2021.

After a period of rapidly declining U.S. COVID-19 incidence during January-March 2021, increases occurred in several jurisdictions (1,2) despite the rapid rollout of a large-scale vaccination program. This increase coincided with the spread of more transmissible variants of SARS-CoV-2, the virus that causes COVID-19, including B.1.1.7 (1,3) and relaxation of COVID-19 prevention strategies such as those for businesses, large-scale gatherings, and educational activities. To provide long-term projections of potential trends in COVID-19 cases, hospitalizations, and deaths, COVID-19 Scenario Modeling Hub teams used a multiple-model approach comprising six models to assess the potential course of COVID-19 in the United States across four scenarios with different vaccination coverage rates and effectiveness estimates and strength and implementation of nonpharmaceutical interventions (NPIs) (public health policies, such as physical distancing and masking) over a 6-month period (April-September 2021) using data available through March 27, 2021 (4). Among the four scenarios, an accelerated decline in NPI adherence (which encapsulates NPI mandates and population behavior) was shown to undermine vaccination-related gains over the subsequent 2-3 months and, in combination with increased transmissibility of new variants, could lead to surges in cases, hospitalizations, and deaths. A sharp decline in cases was projected by July 2021, with a faster decline in the high-vaccination scenarios. High vaccination rates and compliance with public health prevention measures are essential to control the COVID-19 pandemic and to prevent surges in hospitalizations and deaths in the coming months.

Coordinated Strategy for a Model-Based Decision Support Tool for Coronavirus Disease, Utah, USA.

The coronavirus disease pandemic has highlighted the key role epidemiologic models play in supporting public health decision-making. In particular, these models provide estimates of outbreak potential when data are scarce and decision-making is critical and urgent. We document the integrated modeling response used in the US state of Utah early in the coronavirus disease pandemic, which brought together a diverse set of technical experts and public health and healthcare officials and led to an evidence-based response to the pandemic. We describe how we adapted a standard epidemiologic model; harmonized the outputs across modeling groups; and maintained a constant dialogue with policymakers at multiple levels of government to produce timely, evidence-based, and coordinated public health recommendations and interventions during the first wave of the pandemic. This framework continues to support the state's response to ongoing outbreaks and can be applied in other settings to address unique public health challenges.

A scenario modeling pipeline for COVID-19 emergency planning.

Coronavirus disease 2019 (COVID-19) has caused strain on health systems worldwide due to its high mortality rate and the large portion of cases requiring critical care and mechanical ventilation. During these uncertain times, public health decision makers, from city health departments to federal agencies, sought the use of epidemiological models for decision support in allocating resources, developing non-pharmaceutical interventions, and characterizing the dynamics of COVID-19 in their jurisdictions. In response, we developed a flexible scenario modeling pipeline that could quickly tailor models for decision makers seeking to compare projections of epidemic trajectories and healthcare impacts from multiple intervention scenarios in different locations. Here, we present the components and configurable features of the COVID Scenario Pipeline, with a vignette detailing its current use. We also present model limitations and active areas of development to meet ever-changing decision maker needs.

The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology.

The ongoing coronavirus disease 2019 (COVID-19) pandemic has heightened discussion of the use of mobile phone data in outbreak response. Mobile phone data have been proposed to monitor effectiveness of non-pharmaceutical interventions, to assess potential drivers of spatiotemporal spread, and to support contact tracing efforts. While these data may be an important part of COVID-19 response, their use must be considered alongside a careful understanding of the behaviors and populations they capture. Here, we review the different applications for mobile phone data in guiding and evaluating COVID-19 response, the relevance of these applications for infectious disease transmission and control, and potential sources and implications of selection bias in mobile phone data. We also discuss best practices and potential pitfalls for directly integrating the collection, analysis, and interpretation of these data into public health decision making.

The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application.

BACKGROUND: A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities. OBJECTIVE: To estimate the length of the incubation period of COVID-19 and describe its public health implications. DESIGN: Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020. SETTING: News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China. PARTICIPANTS: Persons with confirmed SARS-CoV-2 infection outside Hubei province, China. MEASUREMENTS: Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization. RESULTS: There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine. LIMITATION: Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases. CONCLUSION: This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases. PRIMARY FUNDING SOURCE: U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.

Optimising systemic insecticide use to improve malaria control.

BACKGROUND: Long-lasting insecticidal nets and indoor residual sprays have significantly reduced the burden of malaria. However, several hurdles remain before elimination can be achieved: mosquito vectors have developed resistance to public health insecticides, including pyrethroids, and have altered their biting behaviour to avoid these indoor control tools. Systemic insecticides, drugs applied directly to blood hosts to kill mosquitoes that take a blood meal, offer a promising vector control option. To date, most studies focus on repurposing ivermectin, a drug used extensively to treat river blindness. There is concern that overdependence on a single drug will inevitably repeat past experiences with the rapid spread of pyrethroid resistance in malaria vectors. Diversifying the arsenal of systemic insecticides used for mass drug administration would improve this strategy's sustainability. METHODS: Here, a review was conducted to identify systemic insecticide candidates and consolidate their pharmacokinetic/pharmacodynamic properties. The impact of alternative integrated vector control options and different dosing regimens on malaria transmission reduction are illustrated through mathematical model simulation. RESULTS: The review identified drugs from four classes commonly used in livestock and companion animals: avermectins, milbemycins, isoxazolines and spinosyns. Simulations predicted that isoxazolines and spinosyns are promising candidates for mass drug administration, as they were predicted to need less frequent application than avermectins and milbemycins to maintain mosquitocidal blood concentrations. CONCLUSIONS: These findings will provide a guide for investigating and applying different systemic insecticides to achieve more effective and sustainable control of malaria transmission.

Microfluidics and Metabolomics Reveal Symbiotic Bacterial-Fungal Interactions Between Mortierella elongata and Burkholderia Include Metabolite Exchange.

We identified two poplar (Populus sp.)-associated microbes, the fungus, Mortierella elongata strain AG77, and the bacterium, Burkholderia strain BT03, that mutually promote each other's growth. Using culture assays in concert with a novel microfluidic device to generate time-lapse videos, we found growth specific media differing in pH and pre-conditioned by microbial growth led to increased fungal and bacterial growth rates. Coupling microfluidics and comparative metabolomics data results indicated that observed microbial growth stimulation involves metabolic exchange during two ordered events. The first is an emission of fungal metabolites, including organic acids used or modified by bacteria. A second signal of unknown nature is produced by bacteria which increases fungal growth rates. We find this symbiosis is initiated in part by metabolic exchange involving fungal organic acids.

Treatment of pigs with endectocides as a complementary tool for combating malaria transmission by Anopheles farauti (s.s.) in Papua New Guinea.

BACKGROUND: Outdoor, early-biting, zoophagic behaviours by Anopheles farauti (s.s.) can compromise the effectiveness of bed nets for malaria control. In the Western Pacific region, pigs and dogs represent significant alternative blood sources for mosquitoes. Treating these animals with endectocides may impact mosquito survival and complement control measures. This hypothesis was explored using membrane feeding assays (MFAs), direct feeds on treated pigs, pharmacokinetic analyses and a transmission model. RESULTS: Ivermectin was 375-fold more mosquitocidal than moxidectin (24 h LC50 = 17.8 ng/ml vs 6.7 µg/ml) in MFAs, and reduced mosquito fecundity by > 50% at ≥ 5 ng/ml. Treatment of pigs with subcutaneous doses of 0.6 mg/kg ivermectin caused 100% mosquito mortality 8 days after administration. Lethal effects persisted for up to 15 days after administration (75% death within 10 days). CONCLUSION: The application of these empirical data to a unique malaria transmission model that used a three-host system (humans, pigs and dogs) predicts that the application of ivermectin will cause a significant reduction in the entomological inoculation rate (EIR = 100 to 0.35). However, this is contingent on local malaria vectors sourcing a significant proportion of their blood meals from pigs. This provides significant insights on the benefits of deploying endectocides alongside long-lasting insecticide-treated nets (LLINs) to address residual malaria transmission.

Applying ecological resistance and resilience to dissect bacterial antibiotic responses.

An essential property of microbial communities is the ability to survive a disturbance. Survival can be achieved through resistance, the ability to absorb effects of a disturbance without a notable change, or resilience, the ability to recover after being perturbed by a disturbance. These concepts have long been applied to the analysis of ecological systems, although their interpretations are often subject to debate. Here, we show that this framework readily lends itself to the dissection of the bacterial response to antibiotic treatment, where both terms can be unambiguously defined. The ability to tolerate the antibiotic treatment in the short term corresponds to resistance, which primarily depends on traits associated with individual cells. In contrast, the ability to recover after being perturbed by an antibiotic corresponds to resilience, which primarily depends on traits associated with the population. This framework effectively reveals the phenotypic signatures of bacterial pathogens expressing extended-spectrum ß-lactamases (ESBLs) when treated by a ß-lactam antibiotic. Our analysis has implications for optimizing treatment of these pathogens using a combination of a ß-lactam and a ß-lactamase (Bla) inhibitor. In particular, our results underscore the need to dynamically optimize combination treatments based on the quantitative features of the bacterial response to the antibiotic or the Bla inhibitor.

Robust, linear correlations between growth rates and ß-lactam-mediated lysis rates.

It is widely acknowledged that faster-growing bacteria are killed faster by ß-lactam antibiotics. This notion serves as the foundation for the concept of bacterial persistence: dormant bacterial cells that do not grow are phenotypically tolerant against ß-lactam treatment. Such correlation has often been invoked in the mathematical modeling of bacterial responses to antibiotics. Due to the lack of thorough quantification, however, it is unclear whether and to what extent the bacterial growth rate can predict the lysis rate upon ß-lactam treatment under diverse conditions. Enabled by experimental automation, here we measured >1,000 growth/killing curves for eight combinations of antibiotics and bacterial species and strains, including clinical isolates of bacterial pathogens. We found that the lysis rate of a bacterial population linearly depends on the instantaneous growth rate of the population, regardless of how the latter is modulated. We further demonstrate that this predictive power at the population level can be explained by accounting for bacterial responses to the antibiotic treatment by single cells. This linear dependence of the lysis rate on the growth rate represents a dynamic signature associated with each bacterium-antibiotic pair and serves as the quantitative foundation for designing combination antibiotic therapy and predicting the population-structure change in a population with mixed phenotypes.

Persistence and reversal of plasmid-mediated antibiotic resistance.

In the absence of antibiotic-mediated selection, sensitive bacteria are expected to displace their resistant counterparts if resistance genes are costly. However, many resistance genes persist for long periods in the absence of antibiotics. Horizontal gene transfer (primarily conjugation) could explain this persistence, but it has been suggested that very high conjugation rates would be required. Here, we show that common conjugal plasmids, even when costly, are indeed transferred at sufficiently high rates to be maintained in the absence of antibiotics in Escherichia coli. The notion is applicable to nine plasmids from six major incompatibility groups and mixed populations carrying multiple plasmids. These results suggest that reducing antibiotic use alone is likely insufficient for reversing resistance. Therefore, combining conjugation inhibition and promoting plasmid loss would be an effective strategy to limit conjugation-assisted persistence of antibiotic resistance.

Bacterial temporal dynamics enable optimal design of antibiotic treatment.

There is a critical need to better use existing antibiotics due to the urgent threat of antibiotic resistant bacteria coupled with the reduced effort in developing new antibiotics. ß-lactam antibiotics represent one of the most commonly used classes of antibiotics to treat a broad spectrum of Gram-positive and -negative bacterial pathogens. However, the rise of extended spectrum ß-lactamase (ESBL) producing bacteria has limited the use of ß-lactams. Due to the concern of complex drug responses, many ß-lactams are typically ruled out if ESBL-producing pathogens are detected, even if these pathogens test as susceptible to some ß-lactams. Using quantitative modeling, we show that ß-lactams could still effectively treat pathogens producing low or moderate levels of ESBLs when administered properly. We further develop a metric to guide the design of a dosing protocol to optimize treatment efficiency for any antibiotic-pathogen combination. Ultimately, optimized dosing protocols could allow reintroduction of a repertoire of first-line antibiotics with improved treatment outcomes and preserve last-resort antibiotics.

Collective antibiotic tolerance: mechanisms, dynamics and intervention.

Bacteria have developed resistance against every antibiotic at a rate that is alarming considering the timescale at which new antibiotics are developed. Thus, there is a critical need to use antibiotics more effectively, extend the shelf life of existing antibiotics and minimize their side effects. This requires understanding the mechanisms underlying bacterial drug responses. Past studies have focused on survival in the presence of antibiotics by individual cells, as genetic mutants or persisters. Also important, however, is the fact that a population of bacterial cells can collectively survive antibiotic treatments lethal to individual cells. This tolerance can arise by diverse mechanisms, including resistance-conferring enzyme production, titration-mediated bistable growth inhibition, swarming and interpopulation interactions. These strategies can enable rapid population recovery after antibiotic treatment and provide a time window during which otherwise susceptible bacteria can acquire inheritable genetic resistance. Here, we emphasize the potential for targeting collective antibiotic tolerance behaviors as an antibacterial treatment strategy.