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Effectiveness of International Travel Controls for Delaying Local Outbreaks of COVID-19 – The Maravi Post

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Author affiliations: The University of Hong Kong, Hong Kong, China (B. Yang, Zhanwei Du, T.K. Tsang, B.J. Cowling); University of Melbourne Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia (S.G. Sullivan); Laboratory of Data Discovery for Health Limited, Hong Kong (Z. Du, B.J. Cowling)

International travel control (e.g., screening of inbound travelers, requiring quarantines, and even closing borders) has been a key strategy implemented by many countries to limit importations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, early in the coronavirus disease (COVID-19) pandemic, the World Health Organization (WHO) did not recommend restricting travel (1), and travel controls have not been widely used in previous pandemics (e.g., the 2009–10 influenza pandemic) (2,3). Limiting international movement has enormous social and economic costs, and the benefits of this strategy (i.e., delaying or averting an epidemic) lack real-world evidence. Previous studies, most of which were simulation studies, suggest that travel restrictions can delay but not prevent local epidemics (24).

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Association between international travel controls and local coronavirus disease (COVID-19) outbreaks in 165 countries, January 1–July 31, 2020. A) Temporal distribution of the international travel controls enacted by the studied countries. Data from (7). B) Distribution of the time between a country’s first COVID-19 case and its enactment of any or of the strongest international travel controls. C, D) Probability of reaching first local peak of COVID-19 cases by the time of implementing any (C) or the strongest (D) international travel controls, estimated by using the Kaplan-Meier survival function. Vertical dashed lines in panels B, C, and D indicate the date that Wuhan, China, underwent lockdown; vertical dotted lines indicate the date that the pandemic was declared.

Figure. Association between international travel controls and local coronavirus disease (COVID-19) outbreaks in 165 countries, January 1–July 31, 2020. A) Temporal distribution of the international travel controls enacted by the studied…

To examine the association between implementation of international travel controls and local outbreak progress of COVID-19, we used publicly available data (57; T. Wu et al., unpub. data, https://www.medrxiv.org/content/10.1101/2020.02.25.20027433v1) for January 1–July 31, 2020. Only 14 (8.5%) of the 165 countries studied enacted international travel controls coincident with the lockdown in Wuhan, China (January 23); all controls involved screening inbound travelers (Figure). Enactment of international travel controls peaked ≈3 weeks after WHO declared the pandemic (March 11, 2020), by which time 112 (67.8%) countries completely closed their borders, 44 (26.6%) banned travelers from high-risk regions, and 4 (2.4%) required quarantine for travelers from high-risk regions (Figure; Appendix Figure 1). Of the 165 countries, 90 (54.5%) had imposed at least some restriction before reporting their first COVID-19 case, and 20 (12%) had imposed their strictest restrictions before reporting their first case (Figure; Appendix Figures 1–3).

We determined the progress of outbreaks in each country to be the time from January 1, 2020, to the first epidemic peak, which was identified from the modal daily case counts within any 53-day sliding window (i.e., a quarter of the length of the study period) and needed to comprise >10% of the cumulative incidence during the study period (Appendix Figure 2). By July 31, 2020, the first epidemic peak had been reached in 122 (74%) of the studied countries (Appendix Figure 4). In countries that had enacted any international travel controls before their first COVID-19 case, the first peak was reached an average of 36 days (95% CI 10–61 days) later than it was in countries that did not enact controls until after their first case was reported (p<0.01 by log-rank test; Figure). Countries that implemented their strictest international travel controls before detecting any COVID-19 cases reported their first case a median of 57 days (95% CI 14–70 days) later than countries that imposed their strongest controls after the first case was reported (p = 0.04 by log-rank test; Figure).

After adjusting for population density and implementing nonpharmaceutical interventions by using the accelerated failure time model (Appendix), we estimated that the average time to detection of the first case occurred 1.22 (95% CI 1.06–1.41) times later in countries that implemented any restrictions than in countries that implemented no travel restrictions. This time ratio was extended to 1.31 (95% CI 1.02–1.68) if countries implemented their strongest travel restrictions (Table). Such associations still held when adjusting for time-varying nonpharmaceutical interventions by using the Cox model.

To confirm that these observations were maintained according to alternative measures of epidemic activity, we used the following as outcomes in the models: the time by which COVID-19 deaths first peaked, and attainment of a cumulative incidence of 0.2, 1.0, or 5.0 cases/10,000 persons (by which time peaks had been reached in ≈10%, 30%, and 60% of the countries; Appendix Figure 5). These outcomes may better indicate community spread in countries in which most cases were imported and identified during quarantine (e.g., Fiji), information that was not available from public data. Moreover, outcomes may be better when the epidemic was multimodal (e.g., Guyana) or the country did not experience its main epidemic until later in the study period (e.g., Argentina) (Appendix Figure 2). Both accelerated failure time and Cox models supported earlier observations that enactment of any international travel controls delayed the time in which cumulative incidence rates or deaths peaked. However, enactment of the strongest control was not associated with a reduced time to peak death or cumulative incidence of 5 cases/100,000 persons (Table).

Our work may be influenced by other unmeasured confounders, such as the stringency of international travel controls. We repeated our analyses by removing countries in Asia, in which implementation tended to be more strict, and found that our earlier observations largely held (Appendix Table). In addition, we examined the broader association between international travel controls and local epidemic progression, but we did not examine the roles of specific measures (e.g., quarantine and risk-dependent triage management).

Our findings suggest that implementing international travel controls earlier delayed the initial epidemic peak by ≈5 weeks. Although travel restrictions did not prevent the virus from entering most countries, delaying its introduction bought valuable time for local health systems and governments to prepare to respond to local transmission.

Dr. Yang is a postdoctoral fellow at the School of Public Health, University of Hong Kong. Her research interests are quantifying transmission dynamics and control of infectious diseases.

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We thank the Department of Health of the Food and Health Bureau of the Government of Hong Kong for conducting the outbreak investigation and providing data for analysis.

This project was supported by the Health and Medical Research Fund, Food and Health Bureau and Government of the Hong Kong Special Administrative Region (grant no. COVID190118). The WHO Collaborating Centre for Reference and Research on Influenza is supported by the Australian Government Department of Health.

B.J.C. consults for Roche, GSK, Moderna, AstraZeneca, and Sanofi Pasteur and is supported by the AIR@innoHK program of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government. S.G.S. reports performing unpaid consulting for Sanofi Pasteur and Sequiris. The authors report no other potential conflicts of interest.

All authors are affiliated with WHO collaborating centers. The objective technical analysis and results reported here were not part of official WHO work, and opinions contained herein do not necessarily represent the views of WHO.

The conclusions, findings, and opinions expressed by authors contributing to this journal do not necessarily reflect the official position of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors’ affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.

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PSG-W vs KHA-W Live Score Dream11 Prediction Womens Champions League Paris Saint-Germain Women vs Kharkiv Women

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Are you looking for an extraordinary and exciting football match? we want to tell you about a match which is square between the two most brilliant teams. Here is an upcoming match that will be square off between PSG-W vs KHA-W. As per the details the match will be played on October 13, 2021. Complete detail is here and you will know about every related keypoint like match prediction, dream11 probable lineups and live score as well.

PSG-W vs KHA-W Live Score

Match: PSG-W vs KHA-W
League:
Date: October 13, 2021
Time: 22:15
Venue:  Stade Georges Lefevre, Paris, France

PSG-W vs KHA-W Live Score

In the recent matches, both the teams have played such numbers of matches. As per the details, the team PSG-W has won all five matches in the last five matches played. There is a huge chance that it will maintain the same level of performance in the upcoming match also.

No doubt they will maintain the same level of gameplay and win the upcoming football match also. While discussing another team then we want to tell you that team KHA-W also played such massive and impressive gameplay where they won 4 matches continuously and unfortunately lost 1 match also.

This is a very interesting match and you can watch the live stream of this match on your tv. There are many matches that will be played in the upcoming weeks also. Stay connected with us for more.

Paris Saint-Germain Women:1.Charlotte Voll, 2.Sandy Baltimore, 3.Ashley Lawrence, 4.Paulina Dudek, 5.Jade Le guilly, 6.Launa, 7.Sara Dabritz, 8.Kheira Hamraoui, 9.Lea Khelifi, 10.Ramona Bachmann, 11.Marie Antoinette Katoto

Kharkiv Women: 1.Gamze Yaman, 2.Kristine Aleksanyan, 3.Anastasiia Voronina, 4.Lyubov Shmatko, 5.Anna Petryk, 6.Nadiia Khavanska, 7.Olha Boychenko, 8.Yuliia Shevchuk, 9.Daryna Apanaschenko, 10.Birgul Sadikoglu, 11.Olha Ovdiychuk

As per the available detail in this upcoming match, you will see the team PSG-W will win the match and defeat the opponent team KHA-W. They will do a massive score as we are predicting after watching the recent gameplay.

You can watch the match live stream on the regular broadcaster platform. However, you will see a live score update in this article as well. For further news, stay get in touch with us.



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SARS-CoV-2 Shedding in Semen and Oligozoospermia of Patient with Severe Coronavirus Disease 11 Weeks after Infection – The Maravi Post

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Disclaimer: Early release articles are not considered as final versions. Any changes will be reflected in the online version in the month the article is officially released.

Author affiliations: Columbia Mailman School of Public Health, New York, New York, USA (L.J. Purpura); Columbia University Irving Medical Center, New York (L.J. Purpura, J. Alukal, A.M. Chong, L. Liu, A. Cantos, J. Shah, N. Medrano, J.Y. Chang, M. Tsuji, H. Mohri, A.C. Uhlemann, D. Ho, M.T. Yin)

As of July 2021, >180 million persons worldwide were infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and remain in the convalescent phase (1). Long-term implications for male fertility and potential sexual transmission remain uncertain. However, other emerging pathogens, such as Ebola and Zika viruses, have been shown to undergo sexual transmission (2,3).

Angiotensin-converting enzyme-2 receptors, abundant in the testis (4), are binding sites for SARS-CoV-2, and autopsy reports have demonstrated viral invasion of the testis (5). However, detection of SARS-CoV-2 RNA in semen was not reported from 6 cohort studies that included 213 men (6), although detection has been reported in 2 studies during the acute phase (7,8) and in 1 study during early convalescence (9). We present findings of semen analysis from a prospective coronavirus disease (COVID-19) cohort study.

The Study

A 34-year-old man, who had a history of childhood asthma, was hospitalized for severe coronavirus disease (COVID-19) pneumonia during March 2020. At admission, he had symptoms for 1 week and was positive for SARS-CoV-2 RNA by nasopharyngeal swab specimen reverse transcription PCR (RT-PCR). Chest radiograph showed bilateral interstitial opacities. He was given supplementary oxygen by nasal cannula and completed a 5-day course of hydroxychloroquine but was also given mechanical ventilation on day 10 for respiratory decompensation. His hospital course was complicated by renal failure requiring continuous venovenous hemofiltration. He was eventually extubated on illness day 15 but remained admitted until day 27, when he no longer required supplemental oxygen. Despite his respiratory recovery, he required outpatient dialysis until day 51. At the time of study enrollment (day 72), he had returned to his previous state of health.

Participants were recruited for this prospective cohort study from inpatient and outpatient settings in New York, New York, beginning in March 2020. The study was approved by the institutional review board at Columbia University Irving Medical Center and is registered at Clinicaltrials.gov (NCT04448145). Eligible participants had laboratory confirmation of COVID-19 based on SARS-CoV-2 RT-PCR or serologic testing. Participants completed surveys describing their demographics, underlying conditions, and COVID-19 clinical course. Clinical samples collected at each visit included plasma, peripheral blood mononuclear cells, nasopharyngeal swab specimens, saliva samples, stool/rectal swab specimens, and semen.

We collected and assessed semen per World Health Organization guidelines (10). We instructed participants to clean their hands without spermotoxic lubricants before providing a sample into a sterile container. Samples were frozen after collection. Sperm count was reported in millions per milliliter, and sperm mho provided semen specimens were Hispanic, 2 Black, and 3 Caucasian; 2 had a previous diagnoses of HIV infection, and 2 had body mass index >30 kg/mg2. One participant had had a successful vasectomy and was included in the study to evaluate viral carriage in nontestes accessory organs, such as the prostate gland.

Of the 107 patients enrolled in the cohort study, 7 provided semen specimens (Table 1; Appendix). The mean age of participants was 38.7 (range 32–56) years. Two of the 7 who provided semen specimens were Hispanic, 2 Black, and 3 Caucasian; 2 had a previous diagnoses of HIV infection, and 2 had body mass index >30 kg/mg2. One participant had had a successful vasectomy and was included in the study to evaluate viral carriage in nontestes accessory organs, such as the prostate gland.

A total of 17 semen specimens were collected from 7 participants (Table 1) and underwent quantitative RT-PCR (qRT-PCR) testing and semen analysis. We assessed cycle threshold (Ct) values by using the ZymoBIOMICS DNA/RNA Extraction Kit (Zymoresearch, https://www.zymoresearch.com), the Taqman 4× One-Step Master Mix (Zymoresearch), and SARS-CoV-2 IDT primer/probe sets (Integrated DNA Technologies, https://www.idtdna.com). One sample obtained from participant 1 on day 81 from symptom onset was qRT-PCR positive and had a Ct value of 34.79. Virus isolation was unsuccessful (Appendix). Subsequent semen samples from the participant at days 101 and 169 were negative, as were his saliva, stool, and plasma samples (Table 2). All semen samples from the other 6 men were negative by qRT-PCR.

Participant 1 had severe oligozoospermia and a sperm concentration of <1 million/mL on day 81, followed by a gradual recovery to 16 million/mL on day 101 and 72 million/mL on day 170. All his samples showed sperm motility of 0%, although samples were previously frozen. In addition, 2 other participants had severe oligozoospermia (<5 million/mL) and 1 had mild oligozoospermia (<15 million/mL). Follow-up samples were available for 5 participants. Early sperm count recovery was observed in 3 participants, but 2 participants had a decrease in sperm count later in convalescence, and 1 participant had a count of 16 million/mL at 11 months (Table 1).

Figure 1

IgA, IgM, and IgG antibody responses against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein, receptor-binding domain (RBD), and nucleocapsid protein (NP) for participant 1 at days 72 and 78 from symptom onset. A) C0087 plasma SARS-CoV-2 spike-specific IgA; B) C0087 plasma SARS-CoV-2 spike-specific IgM; C) C0087 plasma SARS-CoV-2 spike-specific IgG; D) C0087 plasma SARS-CoV-2 spike/RBD‒specific IgA; E) C0087 plasma SARS-CoV-2 spike/RBD‒specific IgM; F) C0087 plasma SARS-CoV-2 spike/RBD‒specific IgG; G) C0087 plasma SARS-CoV-2 NP-specific IgA; H) C0087 plasma SARS-CoV-2 NP-specific IgM; I) C0087 plasma SARS-CoV-2 NP-specific IgG. OD450, optical density at 450 nm.

Figure 1. IgA, IgM, and IgG antibody responses against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein, receptor-binding domain (RBD), and nucleocapsid protein (NP) for participant 1 at days 72 and…

Figure 2

10, the response is considered positive. Peptide pool numbers indicate 17-mer overlapping peptides that encompass all 4 proteins. IFN, interferon; PBMCs, peripheral blood mononuclear cells. Error bars indicate mean ± SD.”>

Figure 2. T-cell binding domain responses against membrane, nucleocapsid, spike, and envelope proteins of severe acute respiratory syndrome coronavirus 2, as determined by human IFN-γ ELISpot assay. If the number of spots…

We assessed humoral and cell-mediated immune responses to evaluate level of immunity against SARS-CoV-2. We used immunoassays (11) to quantify IgM, IgG, and IgA binding (half of maximum effect) values against spike trimer and nucleocapsid protein (Figure 1). We performed antibody neutralization assays to measure the neutralization half-maximal inhibitory concentration (Figure 1; Appendix). Half of the maximum effect binding antibody responses and neutralization half-maximal inhibitory concentration against SARS-CoV-2 trimer were modest at days 72 and 78 (Table 2). We used a human interferon-γ ELISpot assay to determine the T-cell response against spike trimer, nucleocapsid, matrix, and envelope proteins at day 81 from symptom onset, which showed strong reactivity against matrix (Figure 2).

Conclusions

We report detection of SARS-CoV-2 RNA by qRT-PCR in semen and severe oligozoospermia in 1 patient 81 days after onset of severe COVID-19. Compared with reports that showed positive RT-PCR findings (79), we provide more granularity regarding clinical course, longitudinal assessment of sperm count, and host immune response.

Detection of SARS-CoV-2 RNA during the late convalescent phase might be attributed to COVID-19 severity, requiring mechanical ventilation and renal replacement therapy for the participant. This feature might have resulted in an enhanced systemic viremic state with subsequent seeding of accessory organs or the testes, an immune-privileged site, in the setting of generalized inflammation, and disruption of the blood–testis barrier (12). This possibility is supported by the participant having adequate humoral and cell-mediated immune responses and associated viral clearance of stool and saliva specimens surrounding the time when SARS-CoV-2 RNA was detected in semen.

In addition, 4 other study participants had oligozoospermia. Sperm count recovery was observed in 2 participants, but the other 2 did not provide longitudinal samples. Numerous reports have suggested a detrimental effect on semen quality after COVID-19 (9,13,14), hypothesized to occur secondary to viral illness and fever causing spermatogenic dysfunction (15). Given this transient insult, it is not unexpected that some of these men showed recovery.

One limitation of our study is that the initial semen sample was collected late in the convalescent phase, and the high Ct value probably indicates detection of inactive virus without risk for sexual transmission. However, if an acute-phase or early convalescent-phase specimen were collected, the Ct value might have been lower. Likewise, semen samples from the other 6 men were also limited to the late convalescence phase and mild acute COVID-19 illnesses, except for 1 participant who required mechanical ventilation, although his status was postvasectomy.

Given the small sample size, we cannot determine the contribution of other known etiologies of oligozoospermia, including obesity and oxygen therapy during hospitalization. In addition, we lacked preinfection semen analysis for comparison, and sperm motility would have been more accurately assessed if performed before freezing. Last, because of inherent difficulty in recruiting for serial semen collection, semen was only collected from a small proportion of participants enrolled in the cohort study. These findings are not generalizable to all male COVID-19 survivors and warrant further research.

In conclusion, SARS-CoV-2 RNA in semen appears to be an extremely rare event, but oligozoospermia has been reported more frequently. Risk factors for viral persistence in the male reproductive tract, longitudinal effects on semen quality, and viral transmission remain to be elucidated, but because of the large number of men in the convalescent phase worldwide, potential effects on reproductive health is not negligible.

Dr. Purpura is an instructor in medicine in the Department of Medicine, Division of Infectious Disease, Columbia University Irving Medical Center, New York, NY. His primary research interests include postinfectious sequelae and emerging pathogens.

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The conclusions, findings, and opinions expressed by authors contributing to this journal do not necessarily reflect the official position of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors’ affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.

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NED-W vs BEL-W Live Score Dream11 Prediction Lineup FIH Women’s Pro League Netherlands Women vs Belgium Women

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Another great and toughest battle between two groups of women will be taking place tonight. Yes, we are talking about FIH Women’s Pro League in which two powerful Hockey teams named Netherlands Women (NED-W) and Belgium Women (BEL-W). The match will be played by both teams on Wednesday, October 13. Now, it will be actually interesting to watch the full battle between both.

NED-W vs BEL-W Live Score

Here, you will get NED-W vs BEL-W Dream11 Prediction, Live Scores, Probable Lineups, and much more information that will help you to know the current statistics of both teams. We collect all the information from some reputed sources.

NED-W vs BEL-W Match Details

  • Match: Netherlands Women (NED-W) and Belgium Women (BEL-W), FIH Women’s Pro League
  • Venue: Wilrijkse Plein Stadium, Antwerp
  • Date and Time: Wednesday, 13th October 2021, 09:30 PM  IST

Now, both teams are deserved to win the upcoming match. Let us tell you that both teams will be going to play their first match in the league. Talking about Netherlands Women (NED-W) then all the players in this team are ready to make the entire environment highly anticipated. Many hockey fans are eagerly waiting to watch the full match between both teams.

On the flip side, Belgium Women (BEL-W) are also ready to make the entire battle very tough for the competitor. All the players are already very talented and experienced who give their best performance in the upcoming match. Belgium Women will be also going to play the first match in the ongoing league. Now, what will happen next will be worth watching. Both teams are considered to be the toughest contenders of the upcoming match.

NED-W vs BEL-W Probable Lineups

Netherlands Women: Sanne Koolen, Lidwej Wells, Xan de Waard, Laurien Leurink, Caia van Maasakker, Margot van Gaffen, Lreen van den Assem, Josine King, Anne Veenendaal, Kelly Jonker, and Lauren Tribe.

Belgium Women: Aisling D’Hooghe, Elena Sotgiu, Stephanie Vanden Borre, Joanne Peeters, Aline Fobe, Tiphaine Duquesne, Emma Puvrej, Link Hillewaert, Lucie Breyne, Michelle Struijk, and Barbara Nelen.

The winning probability of the Belgium Women (BEL-W) is more than Netherlands Women (NED-W). Now, it will be quite impressive to watch the toughest battle between both teams. Here, we will be providing NED-W vs BEL-W Live Scores that will help all Hockey fans to know the live scores during the battle. So, you just need to stay connected with us and refresh the page to get the updated result during the combat.



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