Transmission Dynamics and Short-Term Forecasts of COVID-19: Nepal 2020/2021

Nepal was hard hit by a second wave of COVID-19 from April–May 2021. We investigated the transmission dynamics of COVID-19 at the national and provincial levels by using data on laboratory-confirmed RT-PCR positive cases from the official national situation reports. We performed 8 week-to-week sequential forecasts of 10-days and 20-days at national level using three dynamic phenomenological growth models from 5 March 2021–22 May 2021. We also estimated effective and instantaneous reproduction numbers at national and provincial levels using established methods and evaluated the mobility trends using Google’s mobility data. Our forecast estimates indicated a declining trend of COVID-19 cases in Nepal as of June 2021. Sub-epidemic and Richards models provided reasonable short-term projections of COVID-19 cases based on standard performance metrics. There was a linear pattern in the trajectory of COVID-19 incidence during the first wave (deceleration of growth parameter (p) = 0.41–0.43, reproduction number (Rt) at 1.1 (95% CI: 1.1, 1.2)), and a sub-exponential growth pattern in the second wave (p = 0.61 (95% CI: 0.58, 0.64)) and Rt at 1.3 (95% CI: 1.3, 1.3)). Across provinces, Rt ranged from 1.2 to 1.5 during the early growth phase of the second wave. The instantaneous Rt fluctuated around 1.0 since January 2021 indicating well sustained transmission. The peak in mobility across different areas coincided with an increasing incidence trend of COVID-19. In conclusion, we found that the sub-epidemic and Richards models yielded reasonable short-terms projections of the COVID-19 trajectory in Nepal, which are useful for healthcare utilization planning.

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Development of the reproduction number from coronavirus SARS-CoV-2 case data in Germany and implications for political measures

BMC Medicine ◽

10.1186/s12916-020-01884-4 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Sahamoddin Khailaie ◽

Tanmay Mitra ◽

Arnab Bandyopadhyay ◽

Marta Schips ◽

Pietro Mascheroni ◽

...

Keyword(s):

Structural Changes ◽

Reproduction Number ◽

National Level ◽

Transmission Dynamics ◽

Federal State ◽

Parameter Estimates ◽

Time Varying ◽

Short Term ◽

Robert Koch Institute ◽

Federal States

Abstract Background SARS-CoV-2 has induced a worldwide pandemic and subsequent non-pharmaceutical interventions (NPIs) to control the spread of the virus. As in many countries, the SARS-CoV-2 pandemic in Germany has led to a consecutive roll-out of different NPIs. As these NPIs have (largely unknown) adverse effects, targeting them precisely and monitoring their effectiveness are essential. We developed a compartmental infection dynamics model with specific features of SARS-CoV-2 that allows daily estimation of a time-varying reproduction number and published this information openly since the beginning of April 2020. Here, we present the transmission dynamics in Germany over time to understand the effect of NPIs and allow adaptive forecasts of the epidemic progression. Methods We used a data-driven estimation of the evolution of the reproduction number for viral spreading in Germany as well as in all its federal states using our model. Using parameter estimates from literature and, alternatively, with parameters derived from a fit to the initial phase of COVID-19 spread in different regions of Italy, the model was optimized to fit data from the Robert Koch Institute. Results The time-varying reproduction number (Rt) in Germany decreased to <1 in early April 2020, 2–3 weeks after the implementation of NPIs. Partial release of NPIs both nationally and on federal state level correlated with moderate increases in Rt until August 2020. Implications of state-specific Rt on other states and on national level are characterized. Retrospective evaluation of the model shows excellent agreement with the data and usage of inpatient facilities well within the healthcare limit. While short-term predictions may work for a few weeks, long-term projections are complicated by unpredictable structural changes. Conclusions The estimated fraction of immunized population by August 2020 warns of a renewed outbreak upon release of measures. A low detection rate prolongs the delay reaching a low case incidence number upon release, showing the importance of an effective testing-quarantine strategy. We show that real-time monitoring of transmission dynamics is important to evaluate the extent of the outbreak, short-term projections for the burden on the healthcare system, and their response to policy changes.

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Growth Models for Covid-19 Death Figures of Turkey

Journal of Advances in Medicine and Medical Research ◽

10.9734/jammr/2020/v32i2030675 ◽

2020 ◽

pp. 1-11

Author(s):

Muzaffer Balaban

Keyword(s):

Growth Models ◽

Exponential Model ◽

Prediction Errors ◽

Short Term ◽

Von Bertalanffy Model ◽

Peak Point ◽

Term Forecast ◽

Reported Data ◽

Second Wave ◽

Better Than

This paper presents modeling of the COVID-19 pandemic deaths to understand behavior of it, predict the peak point of the deaths and cases and produces a short-term forecast using the growth models for the reported data of Turkey. The data which is used in this study are gathered of daily announced by Minister of Health. Von Bertalanffy model has outperformed to the other models considered in this study. However, exponential model has predicted the total deaths and total cases better than the others. And, exponential model has given the best prediction errors among them regarding to the death and positive case figures for last months. Observed data have tended to increase since the last days of August. This could mean that the COVİD-19 threat has reached to a critical stage to crack down on prevention of pandemics spread. Or it could sign the beginning of a second wave of epidemics. More studies must be realized to learn more about the pandemic when the new data are available.

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Differing Views of Lodging Reality: Airdna, STR, and Airbnb

Cornell Hospitality Quarterly ◽

10.1177/1938965518777218 ◽

2018 ◽

Vol 60 (3) ◽

pp. 193-199 ◽

Cited By ~ 3

Author(s):

Vinod Agarwal ◽

James V. Koch ◽

Robert M. McNab

Keyword(s):

At Risk ◽

Performance Metrics ◽

Short Term ◽

Virginia Beach ◽

Independent Firm ◽

Using Data ◽

Occupancy Rates ◽

Upward Biases

Airbnb is an Internet-based firm that connects potential short-term renters with hosts who own or control rental properties. Its rapidly expanding activities are tracked by Airdna, an independent firm that generates seemingly conventional performance metrics describing Airbnb. These metrics include occupancy rates, average daily rates, and revenue per available room. However, Airdna does not adhere to long-established STR definitions for these variables. Using data from Virginia Beach, Virginia, we demonstrate that Airdna’s performance metrics exhibit notable upward biases vis-á-vis STR’s metrics. Potential rental hosts, hoteliers, tax collectors, and investors are at risk if they act on the assumption that Airdna’s metrics are comparable with widely understood measures used by STR and tourism experts.

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Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: a data driven approach

BMC Medical Research Methodology ◽

10.1186/s12874-020-01165-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Tarylee Reddy ◽

Ziv Shkedy ◽

Charl Janse van Rensburg ◽

Henry Mwambi ◽

Pravesh Debba ◽

...

Keyword(s):

South Africa ◽

Growth Models ◽

National Level ◽

Growth Curves ◽

Gompertz Model ◽

Logistic Growth ◽

Short Term ◽

Provincial Level ◽

Maximum Period ◽

Data Driven Approach

Abstract Background The rising burden of the ongoing COVID-19 epidemic in South Africa has motivated the application of modeling strategies to predict the COVID-19 cases and deaths. Reliable and accurate short and long-term forecasts of COVID-19 cases and deaths, both at the national and provincial level, are a key aspect of the strategy to handle the COVID-19 epidemic in the country. Methods In this paper we apply the previously validated approach of phenomenological models, fitting several non-linear growth curves (Richards, 3 and 4 parameter logistic, Weibull and Gompertz), to produce short term forecasts of COVID-19 cases and deaths at the national level as well as the provincial level. Using publicly available daily reported cumulative case and death data up until 22 June 2020, we report 5, 10, 15, 20, 25 and 30-day ahead forecasts of cumulative cases and deaths. All predictions are compared to the actual observed values in the forecasting period. Results We observed that all models for cases provided accurate and similar short-term forecasts for a period of 5 days ahead at the national level, and that the three and four parameter logistic growth models provided more accurate forecasts than that obtained from the Richards model 10 days ahead. However, beyond 10 days all models underestimated the cumulative cases. Our forecasts across the models predict an additional 23,551–26,702 cases in 5 days and an additional 47,449–57,358 cases in 10 days. While the three parameter logistic growth model provided the most accurate forecasts of cumulative deaths within the 10 day period, the Gompertz model was able to better capture the changes in cumulative deaths beyond this period. Our forecasts across the models predict an additional 145–437 COVID-19 deaths in 5 days and an additional 243–947 deaths in 10 days. Conclusions By comparing both the predictions of deaths and cases to the observed data in the forecasting period, we found that this modeling approach provides reliable and accurate forecasts for a maximum period of 10 days ahead.

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Precommercial Thinning Increases Spruce Yields in Boreal Mixedwoods in Alberta, Canada

Forests ◽

10.3390/f12040412 ◽

2021 ◽

Vol 12 (4) ◽

pp. 412

Author(s):

Ivan Bjelanovic ◽

Phil Comeau ◽

Sharon Meredith ◽

Brian Roth

Keyword(s):

Growth Models ◽

Growth And Yield ◽

Diameter Growth ◽

Growth Responses ◽

Projection System ◽

Precommercial Thinning ◽

Thinning Intensity ◽

Boreal Mixedwoods ◽

Using Data ◽

Mixedwood Stands

A few studies in young mixedwood stands demonstrate that precommercial thinning of aspen at early ages can improve the growth of spruce and increase stand resilience to drought. However, information on tree and stand responses to thinning in older mixedwood stands is lacking. To address this need, a study was initiated in 2008 in Alberta, Canada in 14 boreal mixedwood stands (seven each at ages 17 and 22). This study investigated growth responses following thinning of aspen to five densities (0, 1000, 2500, 5000 stems ha−1 and unthinned (control)). Measurements were collected in the year of establishment, and three and eight years later. Mortality of aspen in the unthinned plots was greater than in the thinned plots which were not significantly different amongst each other. Eight years following treatment, aspen diameter was positively influenced by thinning, while there was no effect on aspen height. The density of aspen had no significant effect on the survival of planted spruce. Spruce height and diameter growth increased with both aspen thinning intensity and time since treatment. Differentiation among treatments in spruce diameter growth was evident three years from treatment, while differentiation in height was not significant until eight years following treatment. Yield projections using two growth models (Mixedwood Growth Model (MGM) and Growth and Yield Projection System (GYPSY)) were initialized using data from the year eight re-measurements. Results indicate that heavy precommercial aspen thinning (to ~1000 aspen crop trees ha−1) can result in an increase in conifer merchantable volume without reducing aspen volume at the time of harvest. However, light to moderate thinning (to ~2500 aspen stems ha−1 or higher), is unlikely to result in gains in either deciduous or conifer merchantable harvest volume over those of unthinned stands.

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A Skellam regression model for quantifying positional value in soccer

Journal of Quantitative Analysis in Sports ◽

10.1515/jqas-2019-0122 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Konstantinos Pelechrinis ◽

Wayne Winston

Keyword(s):

Regression Model ◽

Video Game ◽

Soccer Player ◽

Performance Metrics ◽

Optical Tracking ◽

General Managers ◽

Annual Salary ◽

Using Data ◽

English Premier League ◽

Positional Value

Abstract Soccer is undeniably the most popular sport world-wide and everyone from general managers and coaching staff to fans and media are interested in evaluating players’ performance. Metrics applied successfully in other sports, such as the (adjusted) +/− that allows for division of credit among a basketball team’s players, exhibit several challenges when applied to soccer due to severe co-linearities. Recently, a number of player evaluation metrics have been developed utilizing optical tracking data, but they are based on proprietary data. In this work, our objective is to develop an open framework that can estimate the expected contribution of a soccer player to his team’s winning chances using publicly available data. In particular, using data from (i) approximately 20,000 games from 11 European leagues over eight seasons, and, (ii) player ratings from the FIFA video game, we estimate through a Skellam regression model the importance of every line (attackers, midfielders, defenders and goalkeeping) in winning a soccer game. We consequently translate the model to expected league points added above a replacement player (eLPAR). This model can further be used as a guide for allocating a team’s salary budget to players based on their expected contributions on the pitch. We showcase similar applications using annual salary data from the English Premier League and identify evidence that in our dataset the market appears to under-value defensive line players relative to goalkeepers.

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A data-driven metapopulation model for the Belgian COVID-19 epidemic: assessing the impact of lockdown and exit strategies

BMC Infectious Diseases ◽

10.1186/s12879-021-06092-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Pietro Coletti ◽

Pieter Libin ◽

Oana Petrof ◽

Lander Willem ◽

Steven Abrams ◽

...

Keyword(s):

Phase 1 ◽

Leisure Activities ◽

National Level ◽

Social Contact ◽

Exit Strategy ◽

Initial Wave ◽

Contact Data ◽

Stochastic Data ◽

The Impact ◽

Second Wave

Abstract Background In response to the ongoing COVID-19 pandemic, several countries adopted measures of social distancing to a different degree. For many countries, after successfully curbing the initial wave, lockdown measures were gradually lifted. In Belgium, such relief started on May 4th with phase 1, followed by several subsequent phases over the next few weeks. Methods We analysed the expected impact of relaxing stringent lockdown measures taken according to the phased Belgian exit strategy. We developed a stochastic, data-informed, meta-population model that accounts for mixing and mobility of the age-structured population of Belgium. The model is calibrated to daily hospitalization data and is able to reproduce the outbreak at the national level. We consider different scenarios for relieving the lockdown, quantified in terms of relative reductions in pre-pandemic social mixing and mobility. We validate our assumptions by making comparisons with social contact data collected during and after the lockdown. Results Our model is able to successfully describe the initial wave of COVID-19 in Belgium and identifies interactions during leisure/other activities as pivotal in the exit strategy. Indeed, we find a smaller impact of school re-openings as compared to restarting leisure activities and re-openings of work places. We also assess the impact of case isolation of new (suspected) infections, and find that it allows re-establishing relatively more social interactions while still ensuring epidemic control. Scenarios predicting a second wave of hospitalizations were not observed, suggesting that the per-contact probability of infection has changed with respect to the pre-lockdown period. Conclusions Contacts during leisure activities are found to be most influential, followed by professional contacts and school contacts, respectively, for an impending second wave of COVID-19. Regular re-assessment of social contacts in the population is therefore crucial to adjust to evolving behavioral changes that can affect epidemic diffusion.

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Higher Handgrip Strength Is Linked to Better Cognitive Performance in Chinese Adults with Hypertension

Brain Sciences ◽

10.3390/brainsci11080985 ◽

2021 ◽

Vol 11 (8) ◽

pp. 985

Author(s):

Shenghua Lu ◽

Fabian Herold ◽

Yanjie Zhang ◽

Yuruo Lei ◽

Arthur F. Kramer ◽

...

Keyword(s):

Cognitive Performance ◽

Short Term Memory ◽

Handgrip Strength ◽

Adult Health ◽

Linear Regression Models ◽

Chinese Adults ◽

Short Term ◽

Term Memory ◽

Using Data ◽

The Relationship

Objective: There is growing evidence that in adults, higher levels of handgrip strength (HGS) are linked to better cognitive performance. However, the relationship between HGS and cognitive performance has not been sufficiently investigated in special cohorts, such as individuals with hypertension who have an intrinsically higher risk of cognitive decline. Thus, the purpose of this study was to examine the relationship between HGS and cognitive performance in adults with hypertension using data from the Global Ageing and Adult Health Survey (SAGE). Methods: A total of 4486 Chinese adults with hypertension from the SAGE were included in this study. Absolute handgrip strength (aHGS in kilograms) was measured using a handheld electronic dynamometer, and cognitive performance was assessed in the domains of short-term memory, delayed memory, and language ability. Multiple linear regression models were fitted to examine the association between relative handgrip strength (rHGS; aHGS divided by body mass index) and measures of cognitive performance. Results: Overall, higher levels of rHGS were associated with higher scores in short-term memory (β = 0.20) and language (β = 0.63) compared with the lowest tertiles of rHGS. In male participants, higher HGS was associated with higher scores in short-term memory (β = 0.31), language (β = 0.64), and delayed memory (β = 0.22). There were no associations between rHGS and cognitive performance measures in females. Conclusion: We observed that a higher level of rHGS was associated with better cognitive performance among hypertensive male individuals. Further studies are needed to investigate the neurobiological mechanisms, including sex-specific differences driving the relationship between measures of HGS and cognitive performance in individuals with hypertension.

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Long-Term Development of Urban Agriculture: Resilience and Sustainability of Farmers Facing the Covid-19 Pandemic in Japan

Sustainability ◽

10.3390/su13084316 ◽

2021 ◽

Vol 13 (8) ◽

pp. 4316

Author(s):

Shingo Yoshida ◽

Hironori Yagi

Keyword(s):

Urban Agriculture ◽

Food Systems ◽

Direct Marketing ◽

Farming System ◽

Long Term Effects ◽

Short Term ◽

Short Supply ◽

Using Data ◽

Urban People

The coronavirus disease 2019 (Covid-19) pandemic has forced global food systems to face unprecedented uncertain shocks even in terms of human health. Urban agriculture is expected to be more resilient because of its short supply chain for urban people and diversified farming activities. However, the short-and long-term effects of the Covid-19 pandemic on urban farms remain unclear. This study aims to reveal the conditions for farm resilience to the Covid-19 pandemic in 2020 and the relationship between short-term farm resilience and long-term farm development using data from a survey of 74 farms located in Tokyo. The results are as follows. First, more than half of the sample farms increased their farm sales during this period. This resilience can be called the “persistence” approach. Second, short-term farm resilience and other sustainable farm activities contributed to improving farmers’ intentions for long-term farm development and farmland preservation. Third, the most important resilience attributes were the direct marketing, entrepreneurship, and social networks of farmers. We discussed the necessity of building farmers’ transformative capabilities for a more resilient urban farming system. These results imply that support to enhance the short-term resilience of urban farms is worth more than the short-term profit of the farms.

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Does Corruption Lead to Lower Subnational Credit Ratings? Fiscal Dependence, Market Reputation, and the Cost of Debt

Business and Politics ◽

10.1017/bap.2020.22 ◽

2021 ◽

pp. 1-19

Author(s):

Maciej Sychowiec ◽

Monika Bauhr ◽

Nicholas Charron

Keyword(s):

Credit Ratings ◽

National Level ◽

Negative Relationship ◽

Cost Of Debt ◽

Economic Implications ◽

Sovereign Credit Ratings ◽

Heterogeneous Effects ◽

Federal Revenue ◽

Negative Effect ◽

Using Data

Abstract While studies show a consistent negative relationship between the level of corruption and range indicators of national-level economic performance, including sovereign credit ratings, we know less about the relationship between corruption and subnational credit ratings. This study suggests that federal transfers allow states with higher levels of corruption to retain good credit ratings, despite the negative economic implications of corruption more broadly, which also allows them to continue to borrow at low costs. Using data on corruption conviction in US states and credit ratings between 2001 and 2015, we show that corruption does not directly reduce credit ratings on average. We find, however, heterogeneous effects, in that there is a negative effect of corruption on credit ratings only in states that have a comparatively low level of fiscal dependence on federal transfers. This suggest that while less dependent states are punished by international assessors when seen as more corrupt, corruption does not affect the ratings of states with higher levels of fiscal dependence on federal revenue.

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