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The continued urbanization poses several challenges for law enforcement agencies to ensure a safe and secure environment. Countries are spending a substantial amount of their budgets to control and prevent crime. However, limited efforts have been made in the crime prediction area due to the deficiency of spatiotemporal crime data. Several machine learning, deep learning, and time series analysis techniques are exploited, but accuracy issues prevail. Thus, this study proposed a Bidirectional Long Short Term Memory (Bi-LSTM) and Exponential Smoothing (ES) hybrid for crime forecasting. The proposed technique is evaluated using New York City crime data from 2010–2017. The proposed approach outperformed as compared to state-of-the-art Seasonal Autoregressive Integrated Moving Averages (SARIMA) with low Mean Absolute Percentage Error (MAPE) (0.3738, 0.3891, 0.3433,0.3964), Root Mean Square Error (RMSE)(13.146, 13.669, 13.104, 13.77), and Mean Absolute Error (MAE) (9.837, 10.896, 10.598, 10.721). Therefore, the proposed technique can help law enforcement agencies to prevent and control crime by forecasting crime patterns. [ABSTRACT FROM AUTHOR]

Background. Colchicine has been used in conditions such as periodic febrile illness, acute pericarditis, and gouty arthritis, all having a common hyperinflammatory response as seen in moderate to severe forms of coronavirus disease 2019 (COVID-19). This project was carried out during the rapid surge of cases in New York City, and the goal was to assess the efficacy of colchicine in treating patients with COVID-19. Methods. Patients admitted to two distinct pulmonary oriented floors of the BronxCare Hospital Center were compared. Patients on one floor were given colchicine in addition to standard of care, while control patients from another floor received only standard of care. Patients who had at least two separate timepoint measurements for at least two out of four serum inflammatory markers (C-reactive protein (CRP), D-dimer, ferritin, or lactate dehydrogenase (LDH)) were selected for the final comprehensive analysis. Results. An initial analysis performed on all patients, irrespective of the availability of two timepoint inflammatory markers, revealed a lower mortality (49.1% versus 72.9%, P = 0.002), a lower percentage of intubations (52.8% versus 73.6%, P = 0.006), and a higher discharge rate (50.9% versus 27.1%, P = 0.002), in the patients who received colchicine. Patients in the final comprehensive analysis groups (34 in the colchicine group and 78 in the control group) had a similar prevalence of comorbid medical conditions, except for renal failure, which was higher in the control group (65.3% versus 35.2%, P = 0.015). HTN (71.8% versus 52.9%, P = 0.053) and DM (51.3% versus 32.4%, P = 0.064) were also more prevalent in the control group, although the difference was not statistically significant. Patients who received colchicine had a lower mortality than the control group (47.1% versus 80.8%, P = 0.0003), lower rate of intubations (47.1% versus 87.2%, P < 0.0001), and a higher discharge rate (52.9% versus 19.2%, P = 0.0003). Patients in the colchicine group also showed a more significant decrease in inflammatory markers for D-dimer (P = 0.037), CRP (P = 0.014), and ferritin (P = 0.012). Conclusions. Our study demonstrates that colchicine improved outcomes in patients with COVID-19 receiving standard of care therapy. Future randomized, placebo-controlled clinical trials to assess the potential benefit of colchicine in COVID-19 are warranted. [ABSTRACT FROM AUTHOR]