Estimating disease transmission in a closed population under repeated testing

The paper presents a novel statistical framework for COVID-19 transmission monitoring and control, which was developed and deployed at The Ohio State University (OSU) main campus in Columbus during the Autumn term of 2020. Our approach effectively handles prevalence data with interval censoring and explicitly incorporates changes in transmission dynamics and human behavior. To illustrate the methodology’s usefulness, we apply it to both synthetic and actual student SARS-CoV-2 testing data collected at the OSU Columbus campus in late 2020

Climate Services For Infectious Disease Control: A Nexus Between Public Health Preparedness And Sustainable Development, Lessons Learned From Long-Term Multi-Site Time-Series Analysis Of Dengue Fever In Vietnam

BACKGROUND: Climate services provide valuable information for making actionable, data-driven decisions to protect public health in a myriad of manners. There is mounting global evidence of the looming threat climate change poses to human health, including the variability and intensity of infectious disease outbreaks in Vietnam and other low-resource and developing areas. In light of the Sustainable Development Goals, this study aimed to examine the utility of spatial and time-series analysis, to inform public health preparedness strategies for sustainable urban development, in terms of dengue epidemiology, surveillance, control, and early warnings. SUBJECTS AND METHODS: Nearly 40 years of spatial and temporal (times-series) dataset of meteorological records, including rainfall, temperature, and humidity (among others) which can be predictors of dengue were assembled for all provinces of Vietnam. This dataset was associated with case data reported to General Department of Preventive Medicine, Ministry of Health of Vietnam, during the same period. Time series of climate and disease variables were analyzed for trend and changing pattern over time. The time-series statistical analysis method sought to identify spatial (when possible) and temporal trend, seasonality, cyclical pattern of disease, and to discover anomalous outbreak events, which departed from expected epidemiological pattern, and corresponding meteorological phenomena, such as El Nino Southern Oscillation (ENSO). RESULTS: Analysis yielded largely converged findings with other locations in South East Asia for larger outbreak years and events such as ENSO. Seasonality, trend, and cycle in many provinces were persistent throughout the dataset, indicating strong potential for climate services to be used in dengue early warnings. CONCLUSION: Public health practitioners, having adequate tools for dengue control available, must plan and budget vector control and patient treatment efforts well in advance of large scale dengue epidemics to curb such events with overall morbidity and mortality. Urban and sustainable development in Vietnam might benefit from evidence linking climate change and ill-health events spatially and temporally in future planning. Long term analysis of dengue case data and meteorological records, provided a cases study evidence for emerging opportunities that on how refined climate services, could contribute to protection of public health. Keywords: dengue, Vietnam, climate change, time-series analysis, climate servic

Assessing Greenhouse Gas Emissions and Health Co-Benefits : A Structured Review of Lifestyle-Related Climate Change Mitigation Strategies

This is the first structured review to identify and summarize research on lifestyle choices that improve health and have the greatest potential to mitigate climate change. Two literature searches were conducted on: (1) active transport health co-benefits, and (2) dietary health co-benefits. Articles needed to quantify both greenhouse gas emissions and health or nutrition outcomes resulting from active transport or diet changes. A data extraction tool (PRISMA) was created for article selection and evaluation. A rubric was devised to assess the biases, limitations and uncertainties of included articles. For active transport 790 articles were retrieved, nine meeting the inclusion criteria. For diet 2524 articles were retrieved, 23 meeting the inclusion criteria. A total of 31 articles were reviewed and assessed using the rubric, as one article met the inclusion criteria for both active transport and diet co-benefits. Methods used to estimate the effect of diet or active transport modification vary greatly precluding meta-analysis. The scale of impact on health and greenhouse gas emissions (GHGE) outcomes depends predominately on the aggressiveness of the diet or active transport scenario modelled, versus the modelling technique. Effective mitigation policies, infrastructure that supports active transport and low GHGE food delivery, plus community engagement are integral in achieving optimal health and GHGE outcomes. Variation in culture, nutritional and health status, plus geographic density will determine which mitigation scenario(s) best suit individual communities

Reviewing estimates of the basic reproduction number for dengue, Zika and chikungunya across global climate zones

BACKGROUND: Globally, dengue, Zika virus, and chikungunya are important viral mosquito-borne diseases that infect millions of people annually. Their geographic range includes not only tropical areas but also sub-tropical and temperate zones such as Japan and Italy. The relative severity of these arboviral disease outbreaks can vary depending on the setting. In this study we explore variation in the epidemiologic potential of outbreaks amongst these climatic zones and arboviruses in order to elucidate potential reasons behind such differences. METHODOLOGY: We reviewed the peer-reviewed literature (PubMed) to obtain basic reproduction number (R0) estimates for dengue, Zika virus, and chikungunya from tropical, sub-tropical and temperate regions. We also computed R0 estimates for temperate and sub-tropical climate zones, based on the outbreak curves in the initial outbreak phase. Lastly we compared these estimates across climate zones, defined by latitude. RESULTS: Of 2115 studies, we reviewed the full text of 128 studies and included 65 studies in our analysis. Our results suggest that the R0 of an arboviral outbreak depends on climate zone, with lower R0 estimates, on average, in temperate zones (R0 = 2.03) compared to tropical (R0 = 3.44) and sub-tropical zones (R0 = 10.29). The variation in R0 was considerable, ranging from 0.16 to 65. The largest R0 was for dengue (65) and was estimated by the Ross-Macdonald model in the tropical zone, whereas the smallest R0 (0.16) was for Zika virus and was estimated statistically from an outbreak curve in the sub-tropical zone. CONCLUSIONS: The results indicate climate zone to be an important determinant of the basic reproduction number, R0, for dengue, Zika virus, and chikungunya. The role of other factors as determinants of R0, such as methods, environmental and social conditions, and disease control, should be further investigated. The results suggest that R0 may increase in temperate regions in response to global warming, and highlight the increasing need for strengthening preparedness and control activities

The evolutionary dynamics of DENV 4 genotype I over a 60-year period

Dengue virus serotype 4 (DENV 4) has had a relatively low prevalence worldwide for decades; however, likely due to data paucity, no study has investigated the epidemiology and evolutionary dynamics of DENV 4 genotype I (DENV 4-I). This study aims to understand the diversity, epidemiology and dynamics of DENV 4-I. We collected 404 full length DENV4-1 envelope (E) gene sequences from 14 countries using two sources: Yunnan Province in China (15 strains during 2013-2016) and GenBank (489 strains up to 2018-01-11). Conducting phylogenetic and phylogeographical analyses, we estimated the virus spread, population dynamics, and selection pressures using different statistical analysis methods (substitution saturation, likelihood mapping, Bayesian coalescent inference, and maximum likelihood estimation). Our results show that during the last 60 years (1956-2016), DENV 4-I was present in mainland and maritime Southeast Asia, the Indian subcontinent, the southern provinces of China, parts of Brazil and Australia. The recent spread of DENV 4-I likely originated in the Philippines and later spread to Thailand. From Thailand, it spread to adjacent countries and eventually the Indian subcontinent. Apparently diverging around years 1957, 1963, 1976 and 1990, the different Clades (Clade I-V) were defined. The mean overall evolution rate of DENV 4-I was 9.74 (95% HPD: 8.68-10.82) x 10(-4) nucleotide substitutions/site/year. The most recent common ancestor for DENV 4-I traces back to 1956. While the demographic history of DENV 4-I fluctuated, peaks appeared around 1982 and 2006. While purifying selection dominated the majority of E-gene evolution of DENV 4-I, positive selection characterized Clade III (Vietnam). DENV 4-I evolved in situ in Southeast Asia and the Indian subcontinent. Thailand and Indian acted as the main and secondary virus distribution hubs globally and regionally. Our phylogenetic analysis highlights the need for strengthened regional cooperation on surveillance and sharing of sample sequences to improve global dengue control and cross-border transmission prevention efforts. Author summary Dengue virus (DENV) can be classified into four serotypes, DENV 1, 2, 3 and 4. Although DENV 4 is the first dengue serotype to diverge in phylogenetic analyses of the genus Flavivirus, this serotype occurs at a low prevalence worldwide and spreads the least rapidly. Similar to other serotypes, DENV 4 can also cause severe dengue (SD) disease manifestations, such as dengue haemorrhagic fever (DHF) and dengue shock syndrome (DSS). To date, no study has investigated the epidemiology and dynamics of DENV 4 genotype I comprehensively. In this study, we seek to address this gap. Our study shows that the distribution of DENV 4-I is mainly restricted to Southeast Asia and the Indian subcontinent. The most recent spread of DENV 4-I likely originated from Southeast Asia-initially circulating in the Philippines, then Thailand and later on the Indian subcontinent. Viruses evolved in situ in Southeast Asia and the Indian subcontinent, respectively. Although DENV 4-I occasionally spread elsewhere, this genotype did not become widely established. The overall evolution rate of DENV 4-I was comparable with that of DENV 2-4. The nucleotide sequences indicates that the demographic history of DENV 4-I fluctuated with peaks apparent during parts of the 1980s and 2000s. Although a weak positive selection existed in Clade III -predominately in Vietnam, purifying selection dominated the E-gene evolution of DENV 4-I

Identification of SARS-CoV-2 variants in indoor dust.

Environmental surveillance of pathogens underlying infectious disease is critical to ensure public health. Recent efforts to track SARS-CoV-2 have utilized wastewater sampling to infer community trends in viral abundance and variant composition. Indoor dust has also been used for building-level inferences, though to date no sequencing data providing variant-scale resolution have been reported from dust samples, and strategies to monitor circulating variants in dust are needed to help inform public health decisions. In this study, we demonstrate that SARS-CoV-2 lineages can be detected and sequenced from indoor bulk dust samples. We collected 93 vacuum bags from April 2021 to March 2022 from buildings on The Ohio State University's (OSU) Columbus campus, and the dust was used to develop and apply an amplicon-based whole-genome sequencing protocol to identify the variants present and estimate their relative abundances. Three variants of concern were detected in the dust: Alpha, Delta, and Omicron. Alpha was found in our earliest sample in April 2021 with an estimated frequency of 100%. Delta was the primary variant present from October of 2021 to January 2022, with an average estimated frequency of 91% (±1.3%). Omicron became the primary variant in January 2022 and was the dominant strain in circulation through March with an estimated frequency of 87% (±3.2%). The detection of these variants on OSU's campus correlates with the circulation of these variants in the surrounding population (Delta p<0.0001 and Omicron p = 0.02). Overall, these results support the hypothesis that dust can be used to track COVID-19 variants in buildings

Tracking COVID-19 lineages in a public library building on a floor-by-floor level.

A) The overall data for the building sampled from any floors. Samples from the same date are averaged together. Next are the floors that were sampled during the collection period: B) Floor 2, C) Floor 3, D) Floor 4, E) Floors 5–11. There is a gap in data from May 2021 to September 2021 due to the summer break and the reduced presence of residential students.</p

Dust collection information.

Environmental surveillance of pathogens underlying infectious disease is critical to ensure public health. Recent efforts to track SARS-CoV-2 have utilized wastewater sampling to infer community trends in viral abundance and variant composition. Indoor dust has also been used for building-level inferences, though to date no sequencing data providing variant-scale resolution have been reported from dust samples, and strategies to monitor circulating variants in dust are needed to help inform public health decisions. In this study, we demonstrate that SARS-CoV-2 lineages can be detected and sequenced from indoor bulk dust samples. We collected 93 vacuum bags from April 2021 to March 2022 from buildings on The Ohio State University’s (OSU) Columbus campus, and the dust was used to develop and apply an amplicon-based whole-genome sequencing protocol to identify the variants present and estimate their relative abundances. Three variants of concern were detected in the dust: Alpha, Delta, and Omicron. Alpha was found in our earliest sample in April 2021 with an estimated frequency of 100%. Delta was the primary variant present from October of 2021 to January 2022, with an average estimated frequency of 91% (±1.3%). Omicron became the primary variant in January 2022 and was the dominant strain in circulation through March with an estimated frequency of 87% (±3.2%). The detection of these variants on OSU’s campus correlates with the circulation of these variants in the surrounding population (Delta p</div

Tracking COVID-19 lineages in a single building.

Samples were collected over a 5-month period with a 1-month gap due to a notable reduced campus residential population during the holidays. Each line represents one sample, and the color of the line indicates the estimated frequency of the lineage present in that sample based on measured mutations. There is a gap from November 2021 to January 2022 due to holiday break and a reduced campus residential population.</p

Primer-probe mix for the qPCR analysis done on the dust samples.

The per plate column corresponds to 110 wells per plate to account for reagent loss. The molar concentrations listed correspond to a total well volume of 20μL.</p