A simple mathematical tool to forecast COVID-19 cumulative case numbers

The impact of the COVID-19 pandemic on neurosurgery in the elderly population in Sweden

BACKGROUND

The COVID-19 pandemic prompted a refocus of health care resources to acute care which has impacted on the capacity of healthcare systems to conduct neurological surgeries. The elderly population has been shown to be particularly vulnerable to the consequences of the pandemic. Less neurosurgery can result in great impact on public health by increasing morbidity and mortality in patients with malignancies and traumatic injuries. The aim of this study was to investigate the effects of the COVID-19 pandemic on neurosurgical procedures in the elderly population in Sweden.

METHODS

In this retrospective observational study, the reported incidence of all neurosurgical procedures registered in the 21 Regions of Sweden during 2015-2021 in people aged 65 year or older was collected. Surgical procedures were classified according to the NOMESCO system of classification. Neurosurgery incidence was defined as the number of NOMESCO associated interventions per 100.000 inhabitants. ICD-10 codes associated with neurosurgery-related diagnoses and deaths were also collected. Expected incidence of neurosurgery, neurosurgery-associated deaths and brain cancer diagnoses was estimated and compared to actual outcomes. Decrease in the incidence of neurosurgery was compared to regional COVID-19 incidence, other types of surgery and surgery waiting times.

RESULTS

The incidence of several categories of neurosurgery decreased in Sweden during 2020 and 2021, although not as much as other surgical categories. Women were more affected than men by the decrease in neurosurgery which could be partly explained by a decrease in brain cancer diagnoses amongst women. There was an association between regional decrease in neurosurgery incidence and longer surgery waiting time. COVID-19 incidence in the region did not have an effect on regional decreases in neurosurgery incidence.

CONCLUSIONS

The COVID-19 pandemic resulted in a reduction in the number of neurosurgical procedures performed in Sweden during 2020-2021, although not as much as in other European countries. There was regional difference in Sweden with respect to number of surgeries, and waiting time for elective surgeries although there was no increase in mortality.

Predicting the distribution of COVID-19 through CGAN-Taking Macau as an example

Machine learning (ML) is an innovative method that is widely used in data prediction. Predicting the COVID-19 distribution using ML is essential for urban security risk assessment and governance. This study uses conditional generative adversarial network (CGAN) to construct a method to predict the COVID-19 hotspot distribution through urban texture and business formats and establishes a relationship between urban elements and COVID-19 so that machines can automatically predict the epidemic hotspots in cities. Taking Macau as an example, this method is used to determine the correlation between the urban texture and business hotspots of Macau and the new epidemic hotspot clusters. Different types of samples afforded different epidemic prediction accuracies. The results show the following: (1) CGAN can accurately predict the distribution area of COVID-19, and the accuracy can exceed 70%. (2) The results of predicting the COVID-19 distribution through urban texture and POI data of hospitals and stations are the best, with an accuracy of more than 60% in experiments in different regions of Macau. (3) The proposed method can also predict other areas in the city that may be at risk of COVID-19 and help urban epidemic prevention and control.

Real-time mortality statistics during the COVID-19 pandemic: A proposal based on Spanish data, January-March, 2021

Objectives

During the COVID-19 pandemic, surveillance systems worldwide underestimated mortality in real time due to longer death reporting lags. In Spain, the mortality monitor "MoMo" published downward biased excess mortality estimates daily. I study the correction of such bias using polynomial regressions in data from January to March 2021 for Spain and the Comunitat Valenciana, the region with the highest excess mortality.

Methods

This adjustment for real-time statistics consisted of (1) estimating forthcoming revisions with polynomial regressions of past revisions, and (2) multiplying the daily-published excess mortality by these estimated revisions. The accuracy of the corrected estimates compared to the original was measured by contrasting their mean absolute errors (MAE) and root mean square errors (RMSE).

Results

Applying quadratic and cubic regressions improved the first communication of cumulative mortality in Spain by 2-3%, on average, and the flow in registered deaths by 20%. However, for the Comunitat Valenciana, those corrections improved the first publications of the cumulative mortality by 36-45%, on average; their second publication, by 23-30%; and the third, by 15-21%. The flow of deaths registered each day improved by 62-63% on their first publication, by 19-36% on the second, and by 12-17% on the third.

Conclusion

It is recommended that MoMo's estimates for excess mortality be corrected from the effect of death reporting lags by using polynomial regressions. This holds for the flows in each date and their cumulative sum, as well as national and regional data. These adjustments can be applied by surveillance systems in other countries.

Phenomenological and mechanistic models for predicting early transmission data of COVID-19

Forecasting future epidemics helps inform policy decisions regarding interventions. During the early coronavirus disease 2019 epidemic period in January-February 2020, limited information was available, and it was too challenging to build detailed mechanistic models reflecting population behavior. This study compared the performance of phenomenological and mechanistic models for forecasting epidemics. For the former, we employed the Richards model and the approximate solution of the susceptible-infected-recovered (SIR) model. For the latter, we examined the exponential growth (with lockdown) model and SIR model with lockdown. The phenomenological models yielded higher root mean square error (RMSE) values than the mechanistic models. When using the numbers from reported data for February 1 and 5, the Richards model had the highest RMSE, whereas when using the February 9 data, the SIR approximation model was the highest. The exponential model with a lockdown effect had the lowest RMSE, except when using the February 9 data. Once interventions or other factors that influence transmission patterns are identified, they should be additionally taken into account to improve forecasting.

Unequal Impact of COVID-19 on Private and Academic Neurosurgical Workforce: Results of an International Survey

Background: Since the COVID-19 outbreak several manuscripts regarding neurosurgical practice during this pandemic have been published. Qualitative studies on how the pandemic affected neurosurgeons, with additional focus on their practice, are still scarce. This study's objective was to investigate the impact of COVID-19 on various aspects of the professional and private life of a homogeneous group of international neurosurgeons affiliated to the European Association of Neurosurgical Societies (EANS). Methods: Neurosurgeons from Europe and abroad were invited to participate in an online survey endorsed by the Individual Membership Committee of the EANS. The survey captured a subjective snapshot of the impact of the first wave of the COVID-19 pandemic on EANS members and was advertised through its Institutional website. In addition to departmental data, personal feeling of safety, financial security, local precautions, number of surgeries performed, changes in daily routine, and other practice-related information were inquired. Differences among practice types were closely reviewed. Results: The survey was distributed between April and May 2020: 204 neurosurgeons participated. Participants were typically active EANS members (73%), consultants (57.9%), from university hospitals (64.5%). Elective surgical practice was still ongoing only for 15% of responders, whereas 18.7% of them had already transitioned to COVID-19 and emergency medical services. While 65.7% of participants thought their institutions were adequately prepared, lack of testing for SARS-CoV-2, and scarcity of personal protective equipment were still a matter of concern for most of them. Overall surgical activity dropped by 68% (cranial by 54%, spine by 71%), and even emergencies decreased by 35%. COVID-19 prompted changes in communication in 74% of departments, 44% increased telemedicine by >50%. While most neurosurgeons had concerns about personal and families' health, financial outlook appeared to be gloomy only for private practitioners. Conclusion: The lockdown imposed in many countries by the COVID-19 outbreak called for immediate modification of working routine and resulted in a dramatic decrease of elective surgical procedures. Neurosurgeons share common concerns but were not equally exposed to the personal health and financial dangers of the ongoing pandemic.