As of November 4, 2023, Malaysian states of Putrajaya and Kuala Lumpur had respectively around 36.1 and 30.6 coronavirus (COVID-19) confirmed cases per 100,000 people, the highest in the country. Malaysia is experiencing a decrease in cases, although the country still expecting a rise due to the highly contagious variant of Omicron.

Malaysia is currently one out of more than 200 countries and territories battling with the novel coronavirus. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

The dataset shows the 7-day median of the RNA copies of the specified virus per day and 100’000 people in the wastewater treatment plant (ARA) Basel as well as the 7-day median of the corresponding case numbers. The data set is usually updated on Tuesdays with the data until the previous Sunday. ProRheno AG (operator of ARA Basel) takes a 24h sample of the raw waste water, which is examined for RNA of the specified viruses by the Cantonal Laboratory Basel-Stadt (KL BS). The measurement methodology has not been changed since the beginning of the monitoring: see publication https://smw.ch/index.php/smw/article/view/3226. The plausibility of the values is continuously checked against internal quality parameters. The study area comprises the catchment area of the ARA Basel, which consists mainly of the canton of Basel-Stadt as well as the municipalities of Allschwil, Binningen, Birsfelden, Bottmingen, Oberwil and Schönenbuch (all Canton Baselland). Until the end of June 2023, the measured values of the KL BS were also presented on the wastewater dashboard of the BAG Covid-19 Switzerland | Coronavirus | Dashboard (https://www.covid19.admin.ch/de/epidemiologic/waste-water?wasteWaterFacility=270101). As of July 2023, the measured values of the EAWAG SARS-CoV2 in wastewater – Eawag (https://www.eawag.ch/de/abteilung/sww/projekte/sars-cov2-im-abwasser/) will be published on this page, which also examines the raw wastewater of ARA Basel. The examination methods used by KL BS and EAWAG are very similar but not identical.Case figures correspond to the number of confirmed and reported cases of infections in the catchment area of ARA Basel.Interpretation of curvesThe monitoring of viruses in wastewater is primarily about identifying trends (in particular, of course, the increase of a circulating virus). It is not possible to derive a certain number of cases or the severity of an infection. A comparison of the curve rash (height of peaks) at different times is hardly possible, because different virus variants lead to different amounts of virus per case. Different virus variants can also affect the symptoms, so that, for example, infections in humans run without a trace, but nevertheless viruses are released into the wastewater.

Research Design and Target PopulationThis research uses purposive sampling to filter the respondents. This study targeted Malaysian citizens who live in Kuala Lumpur and are of both genders and age groups from 18 to 60. A questionnaire was made by using a 5-point Likert scale. The online survey was conducted on the Google Form platform and distributed to Kuala Lumpur regions through Google, Facebook, and WhatsApp. Google Forms is a survey administration software that is included as part of the free, web-based Google Docs Editors suite offered by Google. The researchers in the Kuala Lumpur region spread out the offline survey. All data were collected through an online survey from October 17 to November 15, 2023.Research InstrumentThe survey is adapted from the WHO-RCCE questionnaire (Risk Communication and Community Engagement Tool) from the WHO website. The questionnaire was translated into Malay, tested, and validated by risk management and social science experts. Online questionnaires were distributed as an online survey using social media platforms such as Facebook, WhatsApp, and Twitter in two languages (Malay and English). Results from respondents who are only between 18 and 60 are accepted.

Abstract (our paper)

The spread of COVID-19, the so-called new coronavirus, is currently having an enormous social and economic impact on the entire world. Under such a circumstance, the spread of information about the new coronavirus on SNS is having a significant impact on economic losses and social decision-making. In this study, we investigated how the new type of coronavirus has become a social topic in Japan, and how it has been discussed. In order to determine what kind of impact it had on people, we collected and analyzed Japanese tweets containing words related to the new corona on Twitter. First, we analyzed the bias of users who tweeted. As a result, it is clear that the bias of users who tweeted about the new coronavirus almost disappeared after February 28, 2020, when the new coronavirus landed in Japan and a state of emergency was declared in Hokkaido, and the new corona became a popular topic. Second, we analyzed the emotional words included in tweets to analyze how people feel about the new coronavirus. The results show that the occurrence of a particular social event can change the emotions expressed on social media.

Data

Tweets_YYYY-MM-DD.tsv.gz: The first column is the tweet id, the second column is the date and time (JST) when the tweet was posted, the third column is the flag as to whether the tweet was used for emotion analysis or not, and the fourth column is the tweet id of the retweet source. This data was collected by giving the query "新型肺炎 OR 武漢 OR コロナ OR ウイルス OR ウィルス" to the Twitter Search API. Therefore, most of the tweets are Japanese tweets. We conducted emotion analysis on tweets, excluding retweets and tweets containing links. The fourth column is empty if the tweet is not a retweet.

KL-Divergence.tsv.gz: The first column is the date (JST), and the second column is the value of KL-Divergence that calculated the bias of the users who posted tweets related to COVID-19. The value of KL-Divergence was calculated with all users appearing in Tweets_YYYY-MM-DD.tsv.gz. Based on the sampling stream data, we determined that if the value is below 0.6, there is no bias.

Emotions_by_ML-Ask.tsv.gz: The first column is the date (JST), the second and subsequent columns are the number of tweets for each emotion, and the last column is the number of tweets analyzed for the day. For this analysis, we only used tweets with a value of 1 in the third column of Tweets_YYYY-MM-DD.tsv.gz. We used pymlask (Python implementation of ML-Ask) to estimate the emotion of the tweet.

Publication

This data set was created for our study. If you make use of this data set, please cite: Fujio Toriumi, Takeshi Sakaki, Mitsuo Yoshida. Social Emotions Under the Spread of COVID-19 Using Social Media. Transactions of the Japanese Society for Artificial Intelligence (in Japanese). vol.35, no.4, pp.F-K45_1-7, 2020. 鳥海不二夫, 榊剛史, 吉田光男. ソーシャルメディアを用いた新型コロナ禍における感情変化の分析. 人工知能学会論文誌. vol.35, no.4, pp.F-K45_1-7, 2020. https://doi.org/10.1527/tjsai.F-K45

These deidentified datasets have been approved for public release by the VA Boston Healthcare System's Institutional Review Board and may be used without restriction. Please cite one or more of the source articles when using these data:

Feyman, Y, Auty, SG, Tenso, K, Strombotne, KL, Legler, A, & Griffith, KN. (2022). “County-Level Impact of the COVID-19 Pandemic on Excess Mortality Among U.S. Veterans: A Population-Based Study.” The Lancet Regional Health – Americas 5: 100093.

Tenso, K, Strombotne, KL, Feyman, Y, Auty, SG, Legler, A, & Griffith KN. (in press). “Excess Mortality at Veterans Health Administration Facilities During the COVID-19 Pandemic.” Medical Care.

Avila, CJ, Feyman, Y, Auty, SG, Mulugeta, M, Strombotne, KL, Legler, A, & Griffith, KN. (in progress). “Racial and ethnic disparities in excess mortality due to COVID-19 among U.S. veterans.” Health Services Research.

Demographic and clinical characteristics of HS and COVID-19 patients.

IntroductionEmerging preclinical and clinical studies suggest that altered gut microbiome composition and functions are associated with coronavirus 2019 (COVID- 19) severity and its long-term complications. We hypothesize that COVID-19 outcome is associated with gut microbiome status in population-based settings.MethodsGut metagenomic data of the adult population consisting of 2871 subjects from 16 countries were obtained from ExperimentHub through R, while the dynamic death data of COVID-19 patients between January 22, 2020 and December 8, 2020 in each country was acquired from Johns Hopkins Coronavirus Resource Center. An adjusted stable mortality rate (SMR) was used to represent these countries’ mortality and correlated with the mean relative abundance (mRA) of healthy adult gut microbiome species.ResultsAfter excluding bacterial species with low prevalence (prevalence <0.2 in the included countries), the β-diversity was significantly higher in the countries with high SMR when compared with those with median or low SMR (p <0.001). We then identified the mRA of two butyrate producers, Eubacterium rectale and Roseburia intestinalis, that were negatively correlated with SMR during the study period. And the reduction of these species was associated with severer COVID-19 manifestation.ConclusionPopulation-based microbiome signatures with the stable mortality rate of COVID-19 in different countries suggest that altered gut microbiome composition and functions are associated with mortality of COVID-19.

COVID index in 2020 and 2021.

Protein-Protein, Genetic, and Chemical Interactions for Siu KL (2009):Severe acute respiratory syndrome coronavirus M protein inhibits type I interferon production by impeding the formation of TRAF3.TANK.TBK1/IKKepsilon complex. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Severe acute respiratory syndrome (SARS) coronavirus is highly pathogenic in humans and evades innate immunity at multiple levels. It has evolved various strategies to counteract the production and action of type I interferons, which mobilize the front-line defense against viral infection. In this study we demonstrate that SARS coronavirus M protein inhibits gene transcription of type I interferons. M protein potently antagonizes the activation of interferon-stimulated response element-dependent transcription by double-stranded RNA, RIG-I, MDA5, TBK1, IKKepsilon, and virus-induced signaling adaptor (VISA) but has no influence on the transcriptional activity of this element when IRF3 or IRF7 is overexpressed. M protein physically associates with RIG-I, TBK1, IKKepsilon, and TRAF3 and likely sequesters some of them in membrane-associated cytoplasmic compartments. Consequently, the expression of M protein prevents the formation of TRAF3.TANK.TBK1/IKKepsilon complex and thereby inhibits TBK1/IKKepsilon-dependent activation of IRF3/IRF7 transcription factors. Taken together, our findings reveal a new mechanism by which SARS coronavirus circumvents the production of type I interferons.

Showing the Study design and pre-pandemic and COVID-19 survey dates for each of the longitudinal population studies.

ObjectiveTo analyse if the health progression of geriatric Covid-19 survivors three months after an acute Covid-19 infection was worse than in other geriatric patients. Specifically, we wanted to see if we could see distinct health profiles in the flow of re-admitted Covid-19 patients compared to re-admitted non-Covid-19 controls.DesignMatched cohort study.Setting and participantsElectronic medical records of geriatric patients hospitalised in geriatric clinics in Stockholm, Sweden, between March 2020 and January 2022. Patients readmitted three months after initial admission were selected for the analysis and Covid-19 survivors (n = 895) were compared to age-sex-Charlson comorbidity index (CCI)-matched non-Covid-19 controls (n = 2685).MethodsWe assessed using binary logistic and Cox regression if a previous Covid-19 infection could be a risk factor for worse health progression indicated by the CCI, hospital frailty risk score (HFRS), mortality and specific comorbidities.ResultsThe patients were mostly older than 75 years and, already at baseline, had typically multiple comorbidities. The Covid-19 patients with readmission had mostly had their acute-phase infection in the 1st or 2nd pandemic waves before the vaccinations. The Covid-19 patients did not have worse health after three months compared to the matched controls according to the CCI (odds ratio, OR[95% confidence interval, CI] = 1.12[0.94–1.34]), HFRS (OR[95%CI] = 1.05[0.87–1.26]), 6-months (hazard ratio, HR[95%CI] = 1.04[0.70–1.52]) and 1-year-mortality risk (HR[95%CI] = 0.89[0.71–1.10]), adjusted for age, sex and health at baseline (the CCI and HFRS).Conclusions and implicationsThe overall health progression of re-hospitalized geriatric Covid-19 survivors did not differ dramatically from other re-hospitalized geriatric patients with similar age, sex and health at baseline. Our results emphasize that Covid-19 was especially detrimental for geriatric patients in the acute-phase, but not in the later phase. Further studies including post-vaccination samples are needed.

FigurDer Datensatz zeigt den 7-Tage-Median der RNA-Kopien des angegebenen Virus jeweils pro Tag und 100‘000 Personen im Abwasser der Abwasserreinigungs-Anlage (ARA) Basel sowie den 7-Tage-Median der entsprechenden Fallzahlen. Der Datensatz wird i.d.R. jeweils dienstags mit den Daten bis vorangegangenem Sonntag aktualisiert. In einzelnen Wochen kann es zu Verschiebungen kommen.MessungDie ProRheno AG (Betreiber der ARA Basel) entnimmt jeweils eine 24h-Probe des Rohabwassers, welche durch das Kantonale Laboratorium Basel-Stadt (KL BS) auf RNA der angegebenen Viren untersucht wird. Die Messmethodik wurde dabei seit Beginn des Monitorings nicht verändert: siehe Publikation https://smw.ch/index.php/smw/article/view/3226. Die Plausibilität der Werte wird laufend anhand interner Qualitätsparameter überprüft. Das Untersuchungsgebiet umfasst das Einzugsgebiet der ARA Basel, welches sich hauptsächlich aus dem Kanton Basel-Stadt sowie den Gemeinden Allschwil, Binningen, Birsfelden, Bottmingen, Oberwil und Schönenbuch (alle Kanton Baselland) zusammensetzt. Bis Ende Juni 2023 wurden die Messwerte des KL BS auch auf dem Abwasser-Dashboard des BAG Covid-⁠19 Schweiz | Coronavirus | Dashboard (https://www.covid19.admin.ch/de/epidemiologic/waste-water?wasteWaterFacility=270101) dargestellt. Ab Juli 2023 werden auf dieser Seite die Messwerte der EAWAG SARS-CoV2 im Abwasser - Eawag (https://www.eawag.ch/de/abteilung/sww/projekte/sars-cov2-im-abwasser/) publiziert, welche ebenfalls das Rohabwasser der ARA Basel untersucht. Die vom KL BS und der EAWAG verwendeten Untersuchungsmethoden sind sehr ähnlich aber nicht identisch. Aus diesem Grund kann es zu Abweichungen kommen. Die Messungen werden unabhängig von der EAWAG durch das KL BS weitergeführt, um zeitnähere Messwerte, mit zusätzlichen Normierungsfaktoren und die Flexibilität zur Integration weiterer Analyte zu erhalten.Hinweis: Die ursprünglich dargestellten Werte vom 22.03. bis 01.10.2023 mussten aufgrund einer falschen Einstellung in der Messgerätesoftware, die Einfluss auf die RNA-Quantifizierung hat, nach unten korrigiert werden und sind nun korrekt dargestellt.FallzahlenDie Fallzahlen entsprechen der Anzahl der bestätigten und dem Kanton gemeldeten Fälle der dargestellten Infektionen im Einzugsgebiet der ARA Basel.Interpretation der KurvenBeim Monitoring von Viren im Abwasser geht es in erster Linie darum, Trends zu erkennen (insbesondere natürlich die Zunahme eines zirkulierenden Virus). Es ist nicht möglich, daraus eine bestimmte Fallzahl oder den Schweregrad einer Infektion abzuleiten. Ein Vergleich des Kurvenausschlags (Höhe der Peaks) zu verschiedenen Zeitpunkten ist kaum möglich, da z.B. unterschiedliche Virusvarianten zu unterschiedlichen Virusmengen pro Fall führen. Unterschiedliche Virusvarianten können auch die Symptomatik beeinflussen, so dass z.B. Infektionen bei Menschen spurlos verlaufen, aber dennoch Viren ins Abwasser abgegeben werden.

Covid index categories.

Characteristics of the study population. Categorical variables were described using frequency (n) and proportion (%). Quantitative variables were described using medians (m) and interquartile range (IQR).

Weekday COVI-19 confirmed cases recorded from 1 March 2021 to 22 October 2022 for states of Selangor, KL, Penang, Johor and Sarawak.

Adjusted1 associations between self-reported COVID-19 status and economic stratified by age, sex, NS-SEC, self-reported health and keyworker status using the UKDS full sample.

COVID index comparison between 2020 and 2021.

Ärende: Covid-19_casus_rural Tillgängliga format:.csv och json

Källsystem: Osiris allmänna infektionssjukdomar (AIZ) Filbeskrivning:

Denna fil innehåller följande egenskaper per positivt fall i Nederländerna: Datum för statistik, åldersgrupp, kön, död, dödsvecka, provins, rapportering av GGD

Filen är uppbyggd enligt följande: Ett register för varje laboratorium, bekräftad covid-19-patient i Nederländerna, sedan den första covid-19-anmälan i Nederländerna den 27 februari 2020 (statistiskt datum kan vara tidigare).Filen uppdateras dagligen kl. 16.00, baserat på de uppgifter som registrerats i det nationella systemet för anmälningspliktiga sjukdomar (Osiris AIZ) kl. 10.00 samma dag.

Beskrivning av variabler:

Version: Versionsnummer för datauppsättningen. Om innehållet i datasetet ändras strukturellt (dvs. inte den dagliga uppdateringen eller en korrigering på rekordnivå) kommer versionsnumret att justeras (+ 1) och även motsvarande metadata i RIVMdata (https://data.rivm.nl).

Version 2 uppdatering (20 januari 2022):

— I version 2 av denna datauppsättning är variabeln ’hospital_admission’ inte längre tillgänglig. För antalet sjukhusvistelser hänvisas till Stichting NICE:s registrerade sjukhusvistelser (https://data.rivm.nl/covid-19/COVID-19_ziekenhuisopnames.html).

Date_file: Datum och tidpunkt för offentliggörande av uppgifterna av RIVM

Datum_statistik: Datum för statistik. första sjukdomsdagen, om okänd, datum för laboratoriepositivt, om inte känt, datum för anmälan till GGD (format: åååå-mm-dd)

Date_statistics_type: Datumtyp tillgänglig för variabeln ”Statistiskt datum”, där:

Doo = datum för sjukdomsstart: Första sjukdomsdagen enligt GGD:s rapport. Observera: det är inte alltid känt om denna första dag av sjukdom verkligen påverkade covid-19.

DPL = datum för det första positiva laboratorieresultatet: Datum för det (första) positiva laboratorieresultatet.

Don = datum för anmälan: Datum då GGD mottog anmälan.

Åldersgrupp: Åldersgrupp på livstid. 0–9, 10–19,..., 90+; vid dödsfall <50, 50–59, 60–69, 70–79, 80–89, 90+, Okänt = Okänt

Kön: Kön, Okänd = okänd, Man = Man, Kvinna = Kvinna

Provins: Provinsens namn (baserat på patientens vistelseort)

Avliden: Jag är död. Okänd = Okänd, Ja = Ja, Nej = Nej

Dödsvecka: En veckas död. ÅÅÅÅMM enligt ISO-veckan (börjar från måndag till söndag)

Municipal_health_service: GGD som ringde.

FigurDer Datensatz zeigt den 7-Tage-Median der RNA-Kopien des angegebenen Virus jeweils pro Tag und 100‘000 Personen im Abwasser der Abwasserreinigungs-Anlage (ARA) Basel sowie den 7-Tage-Median der entsprechenden Fallzahlen. Der Datensatz wird i.d.R. jeweils dienstags mit den Daten bis vorangegangenem Sonntag aktualisiert. In einzelnen Wochen kann es zu Verschiebungen kommen.MessungDie ProRheno AG (Betreiber der ARA Basel) entnimmt jeweils eine 24h-Probe des Rohabwassers, welche durch das Kantonale Laboratorium Basel-Stadt (KL BS) auf RNA der angegebenen Viren untersucht wird. Die Messmethodik wurde dabei seit Beginn des Monitorings nicht verändert: siehe Publikation https://smw.ch/index.php/smw/article/view/3226. Die Plausibilität der Werte wird laufend anhand interner Qualitätsparameter überprüft. Das Untersuchungsgebiet umfasst das Einzugsgebiet der ARA Basel, welches sich hauptsächlich aus dem Kanton Basel-Stadt sowie den Gemeinden Allschwil, Binningen, Birsfelden, Bottmingen, Oberwil und Schönenbuch (alle Kanton Baselland) zusammensetzt. Bis Ende Juni 2023 wurden die Messwerte des KL BS auch auf dem Abwasser-Dashboard des BAG Covid-⁠19 Schweiz | Coronavirus | Dashboard (https://www.covid19.admin.ch/de/epidemiologic/waste-water?wasteWaterFacility=270101) dargestellt. Ab Juli 2023 werden auf dieser Seite die Messwerte der EAWAG SARS-CoV2 im Abwasser - Eawag (https://www.eawag.ch/de/abteilung/sww/projekte/sars-cov2-im-abwasser/) publiziert, welche ebenfalls das Rohabwasser der ARA Basel untersucht. Die vom KL BS und der EAWAG verwendeten Untersuchungsmethoden sind sehr ähnlich aber nicht identisch.In den Zeiträumen 29.4. bis 1.8.2022 (ausser 1.6.2022 und 5.6.2022) und 30.5.2023 bis 3.9.2023 wurden keine Abwasserproben auf Influenza und RSV untersucht.Fallzahlen Die Fallzahlen entsprechen der Anzahl der bestätigten und dem Kanton gemeldeten Fälle der dargestellten Infektionen im Einzugsgebiet der ARA Basel.Interpretation der KurvenBeim Monitoring von Viren im Abwasser geht es in erster Linie darum, Trends zu erkennen (insbesondere natürlich die Zunahme eines zirkulierenden Virus). Es ist nicht möglich, daraus eine bestimmte Fallzahl oder den Schweregrad einer Infektion abzuleiten. Ein Vergleich des Kurvenausschlags (Höhe der Peaks) zu verschiedenen Zeitpunkten ist kaum möglich, da z.B. unterschiedliche Virusvarianten zu unterschiedlichen Virusmengen pro Fall führen. Unterschiedliche Virusvarianten können auch die Symptomatik beeinflussen, so dass z.B. Infektionen bei Menschen spurlos verlaufen, aber dennoch Viren ins Abwasser abgegeben werden.

Hier gehts zum Dashboard

Additional file 7: Supplementary Table 1. Subject characteristics and blood measurements.