RESUMEN
OBJECTIVES: The COVID-19 pandemic (caused by SARS-CoV-2) has introduced significant challenges for accurate prediction of population morbidity and mortality by traditional variable-based methods of estimation. Challenges to modelling include inadequate viral physiology comprehension and fluctuating definitions of positivity between national-to-international data. This paper proposes that accurate forecasting of COVID-19 caseload may be best preformed non-parametrically, by vector autoregression (VAR) of verifiable data regionally. METHODS: A non-linear VAR model across 7 major demographically representative New York City (NYC) metropolitan region counties was constructed using verifiable daily COVID-19 caseload data March 12-July 23, 2020. Through association of observed case trends with a series of (county-specific) data-driven dynamic interdependencies (lagged values), a systematically non-assumptive approximation of VAR representation for COVID-19 patterns to-date and prospective upcoming trends was produced. RESULTS: Modified VAR regression of NYC area COVID-19 caseload trends proves highly significant modelling capacity of observed patterns in longitudinal disease incidence (county R2 range: 0.9221-0.9751, all p < 0.001). Predictively, VAR regression of daily caseload results at a county-wide level demonstrates considerable short-term forecasting fidelity (p < 0.001 at one-step ahead) with concurrent capacity for longer-term (tested 11-week period) inferences of consistent, reasonable upcoming patterns from latest (model data update) disease epidemiology. CONCLUSIONS: In contrast to macroscopic variable-assumption projections, regionally-founded VAR modelling may substantially improve projection of short-term community disease burden, reduce potential for biostatistical error, as well as better model epidemiological effects resultant from intervention. Predictive VAR extrapolation of existing public health data at an interdependent regional scale may improve accuracy of current pandemic burden prognoses.
RESUMEN
In this case, a 78-year-old female with no previous medical history of crystalline arthropathy presented with pain, effusion, and erythema about a total knee arthroplasty (TKA) performed 13 years prior. Implementation of a novel synovial fluid alpha-defensin assay ruled out periprosthetic joint infection (PJI) despite a positive 2018 Musculoskeletal Infection Society (MSIS) minor criteria score of 8 points, a significant diagnostic differentiation which prevented secondary invasive debridement or joint irrigation intervention. Confirmatory histologic study was positive for calcium pyrophosphate crystals, indicative of acute pseudogout inflammation rather than PJI or septic arthritis manifestation. The patient was then conservatively managed medically for a pseudogout flare and had no evidence of infection with normal physical exam and laboratory study at one- and two-years post treatment, respectively. Given the predominantly clinical nature of current PJI assessment in-clinic coupled with notable risks associated with aggressive re-intervention in the setting of suspected infection, critical need exists for the maturation of sensitive, reliable empiric measures which may assist in guiding orthopaedic surgeon evaluation of patients presenting with inflammatory symptomology around a previous surgical site. In this case, we conclude that patients with a negative alpha-defensin assay alongside crystalline arthropathy on histology may be cautiously yet successfully treated non-operatively despite clinical MSIS criteria concerning for PJI.