Soft computing techniques for forecasting of COVID-19 in Pakistan

Novel Pandemic COVID-19 led globally to severe health barriers and financial issues in different parts of the world. The forecast on COVID-19 infections is significant. Demeanor vital data will help in executing policies to reduce the number of cases efficiently. Filtering techniques are appropriate for dynamic model structures as it provide reasonable estimates over the recursive Bayesian updates. Kalman Filters, used for controlling epidemics, are valuable in knowing contagious infections. Artificial Neural Networks (ANN) have generally been used for classification and forecasting problems. ANN models show an essential role in several successful applications of neural networks and are commonly used in economic and business studies. Long short-term memory (LSTM) model is one of the most popular technique used in time series analysis. This paper aims to forecast COVID-19 on the basis of ANN, KF, LSTM and SVM methods. We applied ANN, KF, LSTM and SVM for the COVID-19 data in Pakistan to find the number of deaths, confirm cases, and cases of recovery. The three methods were used for prediction, and the results showed the performance of LSTM to be better than that of ANN and KF method. ANN, KF, LSTM and SVM endorsed the COVID-19 data in closely all three scenarios. LSTM, ANN and KF followed the fluctuations of the original data and made close COVID-19 predictions. The results of the three methods helped significantly in the decision-making direction for short term strategies and in the control of the COVID-19 outbreak.

Global Healthcare Resource Efficiency in the Management of COVID-19 Death and Infection Prevalence Rates

The scale of impact of the COVID-19 pandemic on society and the economy globally provides a strong incentive to thoroughly analyze the efficiency of healthcare systems in dealing with the current pandemic and to obtain lessons to prepare healthcare systems to be better prepared for future pandemics. In the absence of a proven vaccine or cure, non-pharmaceutical interventions including social distancing, testing and contact tracing, isolation, and wearing of masks are essential in the fight against the worldwide COVID-19 pandemic. We use data envelopment analysis and data compiled from Worldometers and The World Bank to analyze how efficient the use of resources were to stabilize the rate of infections and minimize death rates in the top 36 countries that represented 90% of global infections and deaths out of 220 countries as of November 11, 2020. This is the first paper to model the technical efficiency of countries in managing the COVID-19 pandemic by modeling death rates and infection rates as undesirable outputs using the approach developed by You and Yan. We find that the average efficiency of global healthcare systems in managing the pandemic is very low, with only six efficient systems out of a total of 36 under the variable returns to scale assumption. This finding suggests that, holding constant the size of their healthcare systems (because countries cannot alter the size of a healthcare system in the short run), most of the sample countries showed low levels of efficiency during this time of managing the pandemic; instead it is suspected that most countries literally "threw" resources at fighting the pandemic, thereby probably raising inefficiency through wasted resource use.

Further Insights on the Migration Biology of Monarch Butterflies, Danaus plexippus (Lepidoptera: Nymphalidae) from the Pacific Northwest

The fall migration of monarch butterflies, Danaus plexippus (L.), in the Pacific Northwest was studied during 2017-2019 by tagging 14,040 captive-reared and 450 wild monarchs. One hundred and twenty-two captive-reared monarchs (0.87%) were recovered at distances averaging 899.9 ± 98.6 km for Washington-released and 630.5 ± 19.9 km for Oregon-released monarchs. The greatest straight-line release to recovery distance was 1392.1 km. A mean travel rate of 20.7 ± 2.2 km/day and maximum travel of 46.1 km/day were recorded. Recovery rates were greater for Oregon-released monarchs (0.92%) than Washington-released (0.34%) or Idaho-released monarchs (0.30%). Most monarchs (106/122) were recovered SSW-S-SSE in California, with 82 at 18 coastal overwintering sites. Two migrants from Oregon were recovered just weeks after release ovipositing in Santa Barbara and Palo Alto, CA. Two migrants released in central Washington recovered up to 360.0 km to the SE, and recoveries from Idaho releases to the S and SE suggests that some Pacific Northwest migrants fly to an alternative overwintering destination. Monarchs released in southern Oregon into smoky, poor quality air appeared to be as successful at reaching overwintering sites and apparently lived just as long as monarchs released into non-smoky, good quality air. Migration and lifespan for monarchs infected with the protozoan parasite, Ophryocystis elektroscirrha (McLaughlin and Myers), appeared to be similar to the migration and survival of uninfected monarchs, although data are limited. Our data improve our understanding of western monarch migration, serving as a basis for further studies and providing information for conservation planning.