Gargi Deshpande

In the United States (US), tick-borne diseases (TBDs) have more than doubled in the past fifteen years and are a major contributor to the overall burden of vector-borne diseases. The most common TBDs in the US-Lyme disease, rickettsioses (including Rocky Mountain spotted fever), and anaplasmosis-have gradually shifted in recent years, resulting in increased morbidity and mortality. In this systematic review, we examined climate change and other environmental factors that have influenced the epidemiology of these TBDs in the US while highlighting the opportunities for a One Health approach to mitigating their impact. We searched Medline Plus, PUBMED, and Google Scholar for studies focused on these three TBDs in the US from January 2018 to August 2023. Data selection and extraction were completed using Covidence, and the risk of bias was assessed with the ROBINS-I tool. The review included 84 papers covering multiple states across the US. We found that climate, seasonality and temporality, and land use are important environmental factors that impact the epidemiology and patterns of TBDs. The emerging trends, influenced by environmental factors, emphasize the need for region-specific research to aid in the prediction and prevention of TBDs.

During the COVID-19 pandemic, wastewater surveillance was used to monitor community transmission of SARS-CoV-2. As new genetic variants emerged, the need for timely identification of these variants in wastewater became an important focus. In response to increased reports of Omicron transmission across the United States, the Oklahoma Wastewater Surveillance team utilized allele-specific RT-qPCR assays to detect and differentiate variants, such as Omicron, from other variants found in wastewater in Oklahoma. The PCR assays showed presence of the Omicron variant in Oklahoma on average two weeks before official reports, which was confirmed through genomic sequencing of selected wastewater samples. Through continued surveillance from November 2021 to January 2022, we also demonstrated the transition from prevalence of the Delta variant to prevalence of the Omicron variant in local communities. We further assessed how this transition correlated with certain demographic factors characterizing each community. Our results highlight RT-qPCR assays as a rapid, simple, and cost-effective method for monitoring the community spread of SARS-CoV-2 genetic variants in wastewater. Additionally, they demonstrate that specific demographic factors such as ethnic composition and household income can correlate with the timing of SARS-CoV-2 variant introduction and spread.

Despite improvements in treatment strategies, breast cancer survival rates in India remain low because of poor awareness and advanced stages at presentation. Early diagnosis and treatment can improve the survival rates of patients with breast cancer. A population-based breast cancer screening programme with mammography is often not feasible in resource-constrained settings. Thermalytix, a novel breast cancer screening technology, applies artificial intelligence algorithms over thermal images to generate an automated interpretation report indicating breast health. The Thermalytix testing equipment is portable, requires minimal skills to operate, and operation is 20 times less expensive than a mammography. The test has been used to screen women in community settings in India. This study was done to assess the performance of the Thermalytix test in identifying women suspicious of breast cancer in community settings.

The COVID-19 pandemic has impacted communities worldwide. Behavioral health providers are at the forefront providing services and are thus vulnerable to psychological sequalae. This study hypothesizes that the fear of COVID-19 predicts depression and anxiety among these providers.

During the COVID-19 pandemic, wastewater surveillance was widely used to monitor temporal and geographical infection trends. Using this as a foundation, a statewide program for routine wastewater monitoring of gastrointestinal pathogens was established in Oklahoma. The results from 18 months of surveillance showed that wastewater concentrations of Salmonella, Campylobacter, and norovirus exhibit similar seasonal patterns to those observed in reported human cases (F = 4–29, p < 0.05) and that wastewater can serve as an early warning tool for increases in cases, offering between one- and two-weeks lead time. Approximately one third of outbreak alerts in wastewater correlated in time with confirmed outbreaks of Salmonella or Campylobacter and our results further indicated that several outbreaks are likely to go undetected through the traditional surveillance approach currently in place. Better understanding of the true distribution and burden of gastrointestinal infections ultimately facilitates better disease prevention and control and reduces the overall socioeconomic and healthcare related impact of these pathogens. In this respect, wastewater represents a unique opportunity for monitoring infections in real-time, without the need for individual human testing. With increasing demands for sustainable and low-cost disease surveillance, the usefulness of wastewater as a long-term method for tracking infectious disease transmission is likely to become even more pronounced.

Conducted a systematic review using Covidence to identify and synthesize modeling approaches applied to Dengue, Chikungunya, and West Nile Virus, along with associated environmental drivers, across studies conducted in the U.S., Europe, and Australia over the past two decades.

Integrated human WNV case data from ArboNET with high-resolution NOAA climate datasets to derive metrics for extreme weather events, and applied Poisson regression models to quantify associations between spatiotemporal climate variability and WNV.

Performed structural equation modeling to assess the relationship between COVID-19 fear and depression using survey data from community behavioral health providers in Oklahoma City.

Performed multivariate, local, and global clustering in ArcGIS to assess associations between malaria cases, ITN use, and antimalarial access and identified spatial autocorrelations as well as malaria hot and cold spots.

Constructed a compartmental SIS model depicting the transmission dynamics of clinical vivax malaria capturing the variance in risk of the host population.

Contributed to the grant application process by assembling subcontract documents, thoroughly reviewing the grant application, and verifying the accuracy of the budget.

Analysis of Spatiotemporal trends of Vector-borne diseases reported to DSHS, Texas (2006-2018) as ICD9 and ICD10 codes using RStudio and Excel. Generated tables, correlation plots and maps representing disease trends.