Richard Grewelle is a current PhD student motivated to understand ecological and evolutionary underpinnings of wildlife disease systems. Prior research areas involve bioinformatics, phylogenetics, and disease ecology. Although with previous experience in terrestrial diseases, including Y. pestis (plague), Richard pursues marine disease ecology due to the lack of knowledge surrounding systems we hardly encounter. Marine diseases present significant challenges to not only biologists; they may devastate fragile ecosystems supporting fisheries or providing ecological services. Richard works to bridge the gap between theoretical and empirical studies, employing population and genetic data to inform theoretical models of disease transmission. Despite the economic significance of this research, conservation of marine species and basic biological understanding are at its heart.
Honors & Awards
ARCS Fellowship, Stanford University (2020-2021)
Stanford Graduate Fellowship, Stanford University (2016-2019)
Education & Certifications
B.S. Hons, University of Kentucky, Chemistry (2016)
B.S. Hons, University of Kentucky, Mathematics (2016)
B.S. Hons, University of Kentucky, Biology (2016)
Statistical Bliss: A novel framework for statistical assessment of drug synergy.
AMER ASSOC CANCER RESEARCH. 2021
View details for Web of Science ID 000680263507193
- Redefining risk in data-poor fisheries FISH AND FISHERIES 2021
- Modeling the efficacy of CRISPR gene drive for schistosomiasis control BioRxiv. 2021
- Data-Poor Ecological Risk Assessment of Multiple Stressors BioRxiv. 2021
Models with environmental drivers offer a plausible mechanism for the rapid spread of infectious disease outbreaks in marine organisms.
2020; 10 (1): 5975
The first signs of sea star wasting disease (SSWD) epidemic occurred in just few months in 2013 along the entire North American Pacific coast. Disease dynamics did not manifest as the typical travelling wave of reaction-diffusion epidemiological model, suggesting that other environmental factors might have played some role. To help explore how external factors might trigger disease, we built a coupled oceanographic-epidemiological model and contrasted three hypotheses on the influence of temperature on disease transmission and pathogenicity. Models that linked mortality to sea surface temperature gave patterns more consistent with observed data on sea star wasting disease, which suggests that environmental stress could explain why some marine diseases seem to spread so fast and have region-wide impacts on host populations.
View details for DOI 10.1038/s41598-020-62118-4
View details for PubMedID 32249775
Estimating the Global Infection Fatality Rate of COVID-19
COVID-19 has become a global pandemic, resulting in nearly three hundred thousand deaths distributed heterogeneously across countries. Estimating the infection fatality rate (IFR) has been elusive due to the presence of asymptomatic or mildly symptomatic infections and lack of testing capacity. We analyze global data to derive the IFR of COVID-19. Estimates of COVID-19 IFR in each country or locality differ due to variable sampling regimes, demographics, and healthcare resources. We present a novel statistical approach based on sampling effort and the reported case fatality rate of each country. The asymptote of this function gives the global IFR. Applying this asymptotic estimator to cumulative COVID-19 data from 139 countries reveals a global IFR of 1.04% (CI: 0.77%,1.38%). Deviation of countries' reported CFR from the estimator does not correlate with demography or per capita GDP, suggesting variation is due to differing testing regimes or reporting guidelines by country. Estimates of IFR through seroprevalence studies and point estimates from case studies or sub-sampled populations are limited by sample coverage and cannot inform a global IFR, as mortality is known to vary dramatically by age and treatment availability. Our estimated IFR aligns with many previous estimates and is the first attempt at a global estimate of COVID-19 IFR.
- Larger viral genome size facilitates emergence of zoonotic diseases BioRxiv. 2020
Gene drives for schistosomiasis transmission control.
PLoS neglected tropical diseases
2019; 13 (12): e0007833
Schistosomiasis is one of the most important and widespread neglected tropical diseases (NTD), with over 200 million people infected in more than 70 countries; the disease has nearly 800 million people at risk in endemic areas. Although mass drug administration is a cost-effective approach to reduce occurrence, extent, and severity of the disease, it does not provide protection to subsequent reinfection. Interventions that target the parasites' intermediate snail hosts are a crucial part of the integrated strategy required to move toward disease elimination. The recent revolution in gene drive technology naturally leads to questions about whether gene drives could be used to efficiently spread schistosome resistance traits in a population of snails and whether gene drives have the potential to contribute to reduced disease transmission in the long run. Responsible implementation of gene drives will require solutions to complex challenges spanning multiple disciplines, from biology to policy. This Review Article presents collected perspectives from practitioners of global health, genome engineering, epidemiology, and snail/schistosome biology and outlines strategies for responsible gene drive technology development, impact measurements of gene drives for schistosomiasis control, and gene drive governance. Success in this arena is a function of many factors, including gene-editing specificity and efficiency, the level of resistance conferred by the gene drive, how fast gene drives may spread in a metapopulation over a complex landscape, ecological sustainability, social equity, and, ultimately, the reduction of infection prevalence in humans. With combined efforts from across the broad global health community, gene drives for schistosomiasis control could fortify our defenses against this devastating disease in the future.
View details for DOI 10.1371/journal.pntd.0007833
View details for PubMedID 31856157
COMPUTER VISION AND MACHINE LEARNING ENABLE ENVIRONMENTAL DIAGNOSTICS FOR TARGETING SCHISTOSOMIASIS CONTROL
AMER SOC TROP MED & HYGIENE. 2018: 418
View details for Web of Science ID 000461386604029
The influence of locus number and information content on species delimitation: an empirical test case in an endangered Mexican salamander
View details for DOI 10.1111/mec.13883