The Sussex COVID-19 Modelling Cell: the methods and successes of a collaboration between public health teams in local authorities, NHS hospital trusts, NHS commissioners, and universities.

BACKGROUND: The Sussex Modelling Cell (SMC) is a consortium, formed during the COVID-19 pandemic, of representatives from NHS Sussex, and the local authorities and universities in Sussex. The SMC aimed to provide public health teams with local-data-driven modelling, data analysis, and policy and commissioning advice to mitigate the impact of the pandemic on the local population. It also aimed to answer operational questions, since the Government's forecasts were not suitably applicable. METHODS: From March 23, 2020, the SMC met (virtually) every Thursday to monitor COVID-19 situation reports, answer queries related to data and modelling, and provide interpretations of data or reports from many internal and external sources. SMC also provided quantitative information for public health teams to use within their organisations to advise on the local epidemic picture. Among other tools, the SMC calibrated a mathematical model to local COVID-19 data that could forecast health-care and hospital demand and COVID-19-related deaths. FINDINGS: Throughout the pandemic, the SMC provided scientific and data-driven evidence on the necessity of body storage contracts, monetary support for urgent care, and operational adjustments surrounding health-care provisions. The scientific evidence was generated and used repeatedly in each organisation to make beneficial decisions in a time of crisis. Although chasing an ever-changing pandemic picture was challenging, our swift reaction to national policy and pandemic changes allowed us to support policymakers, reduce anxiety, and provide clarity on the next steps. Our collaboration is one among few across the country and thus should be not only celebrated but also replicated, with appropriate resources and funding. INTERPRETATION: Besides mitigating the direct impact of the COVID-19 situation in Sussex, we have established a scientific collaboration relationship, in contrast to a customer-consultant setting, allowing the group to incorporate both the technical and applied perspectives into the work. With a clear structure, ethos and methodology, the SMC is able to step into the gap between academia and public health modelling to consider different impactful questions of operational importance where underlying complicated models exist, such as waiting times or system demand and capacity, and provide data analytic upskilling to public health teams. FUNDING: Brighton and Hove City Council, East and West Sussex County Council, and Sussex Health and Care Partnership.

Predicting and forecasting the impact of local outbreaks of COVID-19: use of SEIR-D quantitative epidemiological modelling for healthcare demand and capacity.

BACKGROUND: The world is experiencing local/regional hotspots and spikes in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19 disease. We aimed to formulate an applicable epidemiological model to accurately predict and forecast the impact of local outbreaks of COVID-19 to guide the local healthcare demand and capacity, policy-making and public health decisions. METHODS: The model utilized the aggregated daily COVID-19 situation reports (including counts of daily admissions, discharges and bed occupancy) from the local National Health Service (NHS) hospitals and COVID-19-related weekly deaths in hospitals and other settings in Sussex (population 1.7 million), Southeast England. These data sets corresponded to the first wave of COVID-19 infections from 24 March to 15 June 2020. A novel epidemiological predictive and forecasting model was then derived based on the local/regional surveillance data. Through a rigorous inverse parameter inference approach, the model parameters were estimated by fitting the model to the data in an optimal sense and then subsequent validation. RESULTS: The inferred parameters were physically reasonable and matched up to the widely used parameter values derived from the national data sets by Biggerstaff M, Cowling BJ, Cucunubá ZM et al. (Early insights from statistical and mathematical modeling of key epidemiologic parameters of COVID-19, Emerging infectious diseases. 2020;26(11)). We validate the predictive power of our model by using a subset of the available data and comparing the model predictions for the next 10, 20 and 30 days. The model exhibits a high accuracy in the prediction, even when using only as few as 20 data points for the fitting. CONCLUSIONS: We have demonstrated that by using local/regional data, our predictive and forecasting model can be utilized to guide the local healthcare demand and capacity, policy-making and public health decisions to mitigate the impact of COVID-19 on the local population. Understanding how future COVID-19 spikes/waves could possibly affect the regional populations empowers us to ensure the timely commissioning and organization of services. The flexibility of timings in the model, in combination with other early-warning systems, produces a time frame for these services to prepare and isolate capacity for likely and potential demand within regional hospitals. The model also allows local authorities to plan potential mortuary capacity and understand the burden on crematoria and burial services. The model algorithms have been integrated into a web-based multi-institutional toolkit, which can be used by NHS hospitals, local authorities and public health departments in other regions of the UK and elsewhere. The parameters, which are locally informed, form the basis of predicting and forecasting exercises accounting for different scenarios and impacts of COVID-19 transmission.

Gillnet illumination as an effective measure to reduce sea turtle bycatch.

The growing demand for fish around the world is an immediate threat to marine megafauna that are unintentionally captured in commercial and artisanal fishery operations. Bycatch mitigation strategies, such as turtle excluder devices, circle hooks, and net illumination, have successfully reduced this risk in some fisheries. We explored the effectiveness of gillnet illumination to reduce sea turtle captures in 2 artisanal fisheries (Mankoadze and Winneba, Ghana) under normal fishing conditions. We first quantified sea turtle bycatch in Ghana's artisanal gillnet fishery from 15 boats for 12 months. We then quantified catch of targeted species and sea turtle bycatch from 20 boats for 15 months (7427 net sets). For 10 of these boats, we placed a Centro Economy green light (1 LED) at each 10-m interval on the net. We also quantified target catch and sea turtle bycatch from 30 boats for 8 months (2250 net sets). In 15 of these boats, a Centro Deluxe green light (3 LEDs) was installed at 15-m intervals. Boats with economy lights and those with deluxe lights both exhibited an 81% decrease in sea turtle captures (W = 1, p < 0.001, n = 20; W = 215, p < 0.001, n = 30, respectively) compared with control boats without lights. Illuminated nets resulted in fewer turtle catches for leatherback (Dermochelys coriacea), olive ridley (Lepidochelys olivacea), and green sea turtles (Chelonia mydas) (p < 0.05 for all species). Target catch (mass) (W = 53, p = 0.853 n = 20; W = 76, p = 0.449, n = 23) and value (W = 50, p = 1, n = 20; W = 69, p = 0.728, = 23) were not different across treatments. Our study affirms net illumination can reduce capture rates of 3 species of sea turtles, including the imperiled leatherback. Gear modification methods can successfully reduce bycatch if they are affordable and have broad applications for multiple species in different fisheries.

Iluminación de las Redes Agalleras como una Medida Efectiva para Reducir la Captura Incidental de Tortugas Marinas Resumen La creciente demanda de pescado en todo el mundo es una amenaza inmediata para la megafauna marina que es capturada accidentalmente durante las operaciones de pesca comercial y artesanal. Las estrategias de mitigación de la captura incidental como los dispositivos excluyentes de tortugas, los ganchos circulares y la iluminación de redes han reducido exitosamente este riesgo en algunas pesquerías. Exploramos la efectividad de la iluminación de las redes agalleras para reducir la captura de tortugas marinas en dos pesquerías artesanales (Mankoadze y Winneba, Ghana) bajo condiciones normales de pesca. Primero cuantificamos la captura incidental de tortugas marinas en la pesca artesanal con redes agalleras en Ghana a partir de 15 botes durante doce meses. Después cuantificamos la captura de especies focalizadas y la captura incidental de tortugas marinas a partir de 20 botes durante 15 meses (7,247 conjuntos de redes). En el caso de diez de estos botes, colocamos una luz verde Centro Economy (1 LED) en cada intervalo de 10 m en la red. También cuantificamos la captura focalizada y la captura incidental de tortugas marinas a partir de 30 botes durante 8 meses (2,250 conjuntos de redes). En 15 de estos botes se instaló una luz verde Centro Deluxe (3 LEDs) en cada intervalo de 15 metros. Los botes con las luces Economy y aquellos con las luces Deluxe exhibieron una disminución del 81% en la captura de tortugas marinas (W = 1, p < 0.001, n = 20; W = 215, p < 0.001, n = 30, respectivamente) en comparación con los botes control que no contaban con luces. Las redes iluminadas resultaron en una menor captura de tortugas laúd (Dermochelys coriacea), golfina (Lepidochelys olivacea) y verde (Chelonia mydas) (p < 0.05 para todas las especies). La captura focalizada (masa) (W = 53, p = 0.853 n = 20; W = 76, p = 0.449, n = 23) y el valor focalizado (W = 50, p = 1, n = 20; W = 69, p = 0.728, n = 23) no fueron diferentes entre los tratamientos. Nuestro estudio afirma que la iluminación de las redes puede reducir la tasa de captura de tres especies de tortuga marina, incluyendo a la tortuga laúd que se encuentra en grave peligro de extinción. Los métodos de modificación de equipamiento pueden reducir exitosamente la captura incidental si son asequibles y también tienen aplicaciones amplias para múltiples especies en diferentes industrias pesqueras.

Year-round plasma steroid hormone profiles and the reproductive ecology of gopher tortoises (Gopherus polyphemus) at the southernmost edge of their range.

Populations of wide ranging ectotherms often exhibit variation in traits that are influenced by local environmental conditions. Although the gopher tortoise, Gopherus polyphemus, is well studied in pine flatwoods habitats across their range, little attention has been given to coastal populations existing in the southern extreme portion of the range. We examined the reproductive physiology of a coastal dune population in southwest Florida to determine if reproductive cycles vary across populations. Here we present the first year-round sex hormone profiles for a wild population of gopher tortoises. Male testosterone concentrations varied across the year (F11,54 = 2.52, P = 0.015) with elevated values from September to December and minimal levels from April to July, with the exception of a secondary peak during the month of June. Female testosterone and estradiol concentrations varied across the sampling period (T: F11,66 = 8.54, P < 0.001, E: F11,66 = 4.57, P < 0.001) with highest values from August to February, and lowest levels from May to July. Female progesterone concentrations varied over the year (F11,64 = 3.29, P = 0.002) and increased in late fall with a peak in March. These data suggest this population has an extended breeding season from fall through spring with mating likely occurring from September through March, and nesting in winter through spring. This pattern is similar to reproductive patterns described for tropical and sub-tropical chelonians but differs from that of gopher tortoise populations in northern portions of the range where hibernation may last for five months and a single clutch of eggs are deposited in late spring.

Geographic variation in egg size and lipid provisioning in the diamondback terrapin Malaclemys terrapin.

Understanding phenotypic differentiation among populations of wide-ranging species remains at the core of life-history research, because adaptation to local environmental conditions is expected. For example, when energy resources influence offspring fitness (as in oviparous ectotherms), the egg and hatchling environments are expected to influence selection by acting on the amount of energy allocated to offspring. Here we identify population variation in egg mass, length, width, and volume from diamondback terrapin Malaclemys terrapin eggs collected in Rhode Island (RI), Maryland (MD), and South Carolina (SC). Egg size (mean volume: 7.6, 8.1, and 9.1 cc in RI, MD, and SC, respectively) and clutch size (mean no. eggs: 16.1, 12.2, and 6.0 in RI, MD, and SC, respectively) differed among populations, which indicated that females produce larger clutches with smaller eggs at high latitudes and smaller clutches of larger eggs at lower latitudes. Lipid analyses indicated that eggs from SC contained yolks with a higher proportion of nonpolar lipids than did eggs from MD or RI (mean percentage of nonpolar lipids: 22.3%, 22.5%, and 31.8% in RI, MD, and SC, respectively). Thus, female terrapins in SC are laying larger eggs with increased lipid content to provide more energy for the developing embryo. Interestingly, total triacylglycerol (energetic lipid) was greater in southern populations but occurred in higher proportions in northern populations (total triacylglycerol: 88.0%, 85.4%, and 81.9% in RI, MD, and SC, respectively). This variation in triacylglycerol levels demonstrates the necessity for quantifying each lipid component. These data indicate a difference in reproductive strategy by which females in northern populations invest in higher fecundity with less energetic resources per offspring, whereas females in southern populations invest in larger eggs with considerably greater energy reserves.