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.

Ultraviolet Radiation-Induced Downregulation of SERCA2 Mediates Activation of NLRP3 Inflammasome in Basal Cell Carcinoma.

Basal cell carcinomas (BCCs) account for majority of skin malignancies in the United States. The incidence of BCCs is strongly associated with exposure of ultraviolet (UV) radiation. Nucleotide-binding domain, leucine-rich-repeat-containing family, pyrin domain-containing 3 (NLRP3) inflammasome plays an important role in innate immune responses. Different stimuli such as toxins, microorganisms and particles released from injured cells activate the NLRP3 inflammasome. Activated NLRP3 results in activation of caspase-1, which cleaves pro-IL-1ß to active IL-1ß. In this study, we have shown that NLRP3 is expressed in human basal cell carcinomas. The proximal steps in activation of NLRP3 inflammasome are not well understood. Here, we have attempted to elucidate a critical role for Ca2+ mobilization in activation of the NLRP3 inflammasome by UVB exposure using HaCaT keratinocytes. We have demonstrated that UVB exposure blocks Ca2+ mobilization by downregulating the expression of sarco/endoplasmic reticulum Ca2+ -ATPase (SERCA2), a component of store-operated Ca2+ entry that leads to activation of the NLRP3 inflammasome.

The intersection between physical health and mental health: a global perspective.

Mental, physical, and social health are closely interwoven and interdependent. Recent research and better understanding of the relationships between mental, physical, and social health indicate that each is crucial to the overall well-being of individuals, societies, and countries. Because mental health and mental disorders have been ignored or neglected in many parts of the world, the reciprocal impacts between physical health and mental health are not readily apparent. Providing treatment in primary care would enable the largest number of people to get easier and faster access to services. Current health personnel should be trained in the essential skills of mental health care, and mental health should be included in health training curricula.