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RIVM rapport:Luchtverontreiniging is schadelijk voor de gezondheid. Het RIVM heeft onderzocht of blootstelling aan luchtvervuiling door fijnstof en stikstofdioxide invloed heeft op de kans om besmet te raken met het coronavirus en op de kans om daar ernstig ziek van te worden. Onder ernstig ziek verstaan we dat iemand in het ziekenhuis moet worden opgenomen of aan de ziekte overlijdt. Mensen die aan hoge concentraties luchtverontreiniging blootstonden, blijken een grotere kans te hebben om besmet te raken. Ook was hierdoor de kans groter dat mensen door corona in het ziekenhuis moesten worden opgenomen omdat ze erg ziek werden. Ten slotte was de kans dat ze aan corona stierven groter. Deze effecten zijn te zien kort (een tot twee weken) na een periode van meer luchtverontreiniging. Dat is ook zo wanneer mensen jaren wonen op een plek met meer luchtverontreiniging. Deze resultaten bevestigen eerdere conclusies uit internationaal onderzoek. Ook blijkt dat bij een langdurige blootstelling aan luchtverontreiniging de kans op een corona-infectie hetzelfde is als die op andere luchtwegaandoeningen. Denk aan andere luchtweginfecties met klachten die lijken op corona. Dat luchtverontreiniging in het algemeen een grotere kans geeft op luchtwegaandoeningen was al bekend. In het onderzoek is gekeken naar drie belangrijke bronnen van fijnstof in Nederland: landbouw, wegverkeer en industrie. Deze bronnen hebben invloed op de luchtkwaliteit. Ook verschilt per bron de samenstelling van fijnstof, en daarmee de schadelijkheid. Fijnstof van de veehouderij draagt bij aan zowel de kans op besmetting als de ernst van de ziekte. Fijnstof van wegverkeer lijkt meer invloed te hebben op de ernst van de ziekte en minder op de kans om met het virus besmet te raken. Fijnstof van industrie lijkt niet bij te dragen. De resultaten van het onderzoek ondersteunen het beleid van de Nederlandse overheid om de luchtkwaliteit te verbeteren. Het RIVM heeft dit onderzoek gedaan met de Universiteit Utrecht, Wageningen, Air pollution is harmful to health. RIVM has examined whether exposure to air pollution from particulate matter and nitrogen dioxide increases the risks of getting infected with SARS-CoV-2 and falling severely ill. Severe illness was defined as a person requiring hospitalisation or dying as a result of COVID-19. This study found that people who were exposed to high concentrations of air pollution had an increased risk of getting infected with SARS-CoV-2. This exposure also increased the risk of hospital admission or death due to COVID-19. These effects were observed shortly (within one to two weeks) after exposure to elevated concentrations of air pollution. This was also the case for people who had been living for years in locations with elevated concentrations of air pollution. These results confirm the findings of previous international studies. It was also found that, among people under long-term exposure to air pollution, the risk of getting infected with SARS-CoV-2 was not different from that of developing other respiratory diseases, such as those causing symptoms similar to those of SARS-CoV-2 infection. It was already known that exposure to air pollution increases the risk of developing respiratory diseases in general. This study also looked at three major sources of particulate matter emissions in the Netherlands: agriculture, road traffic and industry. These sources have an impact on air quality. In addition, the composition of particle matter emissions and therefore their harmfulness differ per source. Exposure to particular matter emissions from agriculture increases both the risk of getting infected and the severity of the illness. It was found that particular matter emissions from road traffic have a larger effect on the severity of COVID-19 but less of an effect on the risk of getting infected with SARS-CoV-2. Particular matter emissions from industry did not appear to contribute significantly to either. The results of this study support the policy of

RIVM rapport:De Nederlandse maatregelen tijdens de coronacrisis werkten goed om het aantal besmettingen met het virus SARS-Cov-2 te verminderen. Dat blijkt uit onderzoek van het RIVM. Dit waren maatregelen die ervoor zorgden dat mensen het virus zo min mogelijk aan elkaar doorgaven. Bijvoorbeeld door anderhalve meter afstand van elkaar te houden en de sluiting van scholen. De resultaten van dit onderzoek helpen het RIVM om bij een volgende grote ziekte-uitbraak snel te adviseren over maatregelen en beleid.? Hoe goed de contactmaatregelen werkten, is niet per losse maatregel te onderzoeken. Dat komt omdat er vaak meerdere tegelijk golden. Wel kun je kijken naar de effecten van verschillende pakketten van maatregelen. Voor Nederland bleek: hoe zwaarder het pakket aan maatregelen als geheel, hoe effectiever. Ook is het moment waarop de maatregelen ingingen heel belangrijk. Hoe sneller ze na het begin van de eerste coronagolf werden ingevoerd, hoe meer sterfgevallen zijn voorkomen. ? Uit het onderzoek komen een aantal adviezen over maatregelpakketten. Een daarvan is om bepaalde gegevens van besmette personen te verzamelen. Zoals wie de meest waarschijnlijke besmetter is, wat het meest waarschijnlijke moment van de besmetting is en de eerste ziektedag. Met deze informatie kan worden bepaald hoeveel tijd er zit tussen besmet raken en ziek worden. En hoe lang het duurt voordat een besmet persoon iemand anders besmet. Een ander advies is om zo snel mogelijk goed inzicht te krijgen hoe vaak mensen contact hebben met anderen in een samenleving en hoe mensen zich verplaatsen. Dit is nodig om beter te kunnen bepalen welke maatregelen het beste de verspreiding van een virus kunnen tegengaan.?? Vanaf januari 2021 is in Nederland gestart met vaccinatie tegen COVID19. In binnen- en buitenland is onderzocht in hoeverre de vaccins mensen hebben beschermd tegen COVID-19, de ziekte die het virus veroorzaakt. Daaruit blijkt dat gevaccineerde mensen minder vaak besmet raken dan ongevaccineerde mensen, The measures implemented in the Netherlands during the COVID-19 crisis were effective in reducing infections with the SARS-CoV-2 virus. This is the outcome of a study conducted by RIVM. The measures were intended to minimise the risk of people transmitting the virus to others, for example by means of social distancing and by closing schools. The results of this study will help RIVM in providing rapid advice on measures and policy in the event of a new major outbreak of disease.? The effectiveness of contact measures cannot be determined at the level of individual measures. This is because often several measures were in force at the same time. However, it is possible to look at the effects of different packages of measures. In the Netherlands it was found that the more severe the package of measures as a whole, the greater the effect. The timing of the measures was also very important. The faster the measures were introduced after the start of the first wave of infections, the more deaths they were able to prevent. ? The study has also yielded a number of recommendations on such packages of measures. One of these recommendations is to collect specific data on people who are infected, such as data on the most likely source of infection, the most likely time of exposure, and the first day of illness. This information can be used to calculate the amount of time between the moment of exposure and the onset of illness, and the time it takes before an infected person can infect another person. Another recommendation is to obtain a clear picture, as soon as possible, of the frequency of contacts between individuals in society and of the ways in which people move about. This information will improve our ability to identify the measures that are most effective in curbing the spread of a virus.?? In the Netherlands, vaccinations against COVID-19 started in January 2021. Researchers in the Netherlands and abroad have studied the extent to which the vaccines helped to protect pe

RIVM rapport:During the COVID-19 pandemic non-pharmaceutical interventions (NPIs) were taken to mitigate virus spread. Many of these measures were taken together and interacted with each other, and compliance may have changed over the course of the pandemic, making it difficult to disentangle the effectiveness of single measures. It can be more meaningful to consider the overall effectiveness of sets of NPIs during the pandemic. We estimate the overall effectiveness of sets of NPIs in reducing transmission by comparing the observed reproduction number, which is the number of secondary infections caused by a typically infected person, to a counterfactual reproduction number if no NPIs were taken. The counterfactual reproduction number is based on a reproduction number that accounts for seasonal variations in transmissibility, for emergence of more transmissible variants, and for changes in immunity in the population. The population immunity is reconstructed from longitudinal serological surveys and vaccination coverage data, taking immunity waning after infection and vaccination into account. We estimate the effectiveness of NPIs as taken in the Netherlands from the start of the pandemic in March 2020 until the emergence of the Omicron variant in November 2021. We find that the effectiveness of NPIs was high in March and April 2020 during the first pandemic wave and it was high in January and February 2021, coinciding with the two periods with the most stringent measures. For both periods the effectiveness was estimated at approximately 50%, i.e. without any measures the reproduction number would have been twice as high as observed. This approach to estimate overall effectiveness of NPIs against transmission over time combines data from different sources, while making relatively few assumptions on the transmission process. This method can be applied to any region with sufficient data to 2 reconstruct the population immunity. Also during a future pandemic of any directly transmitt

Migratory birds are an increasing focus of interest when it comes to infection dynamics and the spread of avian influenza viruses (AIV). However, we lack detailed understanding of migratory birds’ contribution to local AIV prevalence levels and their downstream socio‐economic costs and threats. To explain the potential differential roles of migratory and resident birds in local AIV infection dynamics, we used a susceptible‐infectious‐recovered (SIR) model. We investigated five (mutually non‐ exclusive) mechanisms potentially driving observed prevalence patterns: (1) a pronounced birth pulse (e.g. the synchronised annual influx of immunologically naïve individuals), (2) short‐term immunity, (3) increase in susceptible migrants, (4) differential susceptibility to infection (i.e. transmission rate) for migrants and residents, and (5) replacement of migrants during peak migration. SIR models describing all possible combinations of the five mechanisms were fitted to individual AIV infection data from a detailed longitudinal surveillance study in the partially migratory mallard duck (Anas platyrhynchos). During autumn and winter, the local resident mallard community also held migratory mallards that exhibited distinct AIV infection dynamics. Replacement of migratory birds during peak migration in autumn was found to be the most important mechanism driving the variation in local AIV infection patterns. This suggests that a constant influx of migratory birds, likely immunological naïve to locally circulating AIV strains, is required to predict the observed temporal prevalence patterns and the distinct differences in prevalence between residents and migrants. Synthesis and applications. Our analysis reveals a key mechanism that could explain the amplifying role of migratory birds in local avian influenza virus infection dynamics; the constant flow and replacement of migratory birds during peak migration. Apart from monitoring efforts, in order to achieve adeq

We implement and evaluate methods to infer the phylogeny of Variable Number of Tandem Repeats (VNTR) isolates of tuberculosis through Bayesian inference and Markov Chain Monte Carlo, using an existing transition rate matrix (Sainudiin et al., 2004). By also inferring the phylogeny through the model of Hasegawa, Kishino and Yano (HKY) using nucleotide data of the same isolates, we are able quantitatively and qualitatively compare the phylogenies obtained through both models. By simulating data, we assess how well the true phylogeny can be inferred for both the Sainudiin and HKY model, for different levels of mutational saturation in the data. We show how both the Sainudiin and HKY model can be combined to yield a phylogeny that is better resolved and more accurate than by the use of either model. By changing the model for the mutation rate proportionality in the Sainudiin model, we are able to use the estimates of the model parameters to speculate on the mechanisms by which VNTR mutates. The developed methods have been made available in the package BEASTvntr for beast2.

Purpose: The purpose of the study was to survey African Horse Sickness (AHS) virus prevalence in healthy horses and identify the serotypes circulating in Senegal. This study was conducted one month prior to the notification of the first AHS clinical case in northern Senegal and before mass vaccination against eight serotypes took place. Methods & Materials: Between 12-19 August 2007, 414 horses were randomly sampled in three zones in the Senegal River delta: St.-Louis, Ross Bethio and Richard Toll. The blood samples collected from 138 horses in each zone were centrifuged, stored at -20 °C, and later tested by competitive ELISA. A random sample of 71 positive horse sera was analyzed by Plaque Reduction Neutralization Test (PRNT) on Vero cells against African Horse Sickness Virus (AHSV) serotypes. Cytopathogenic effects were read four days later. Statistical data analysis was done using R software. Results: The overall true seroprevalence rate by ELISA test was 89.5% in 414 horse sera. In the subset of 71 infected horses, antibodies against AHSV 2 were detected in 67.5%, against AHSV 7 in 92.96% and against AHSV 9 in 100% of horses. AHSV 7 antibodies were found in 80.95% of the positive horses from Ross-Bethio, 95.45% from St.-Louis, and 100% in Richard-Toll zones. Conclusion: Prior to this study, only the monovalent vaccine against AHSV serotype 9 was used on horses in the area. The polyvalent vaccine against eight types of AHSV was first used after our sampling. Our results reveal the presence of antibodies against AHSV 7, which suggests that Serotype 7 virus had been circulating prior to the mass vaccination. Chi-square test for the three serotypes showed that they are not independent. Based on these results we hypothesize that the vectors of AHSV 9 in the Senegal River delta may be the same vectors that introduced and spread AHSV 2 and AHSV 7. This work is the first description of AHSV 7 in Senegal.

Cryptosporidium is a protozoa, which affects animals and human worldwide. This project is part of a broader study looking at the zoonotic aspect of Cryptosporidium spp. at the wildlife/livestock and human interface of the KNP. The purpose of this study is to detect the prevalence and risk factors of cryptosporidiosis for calves living in a rural farming area close to the KNP. Therefore we collected 354 fecal samples of individual calves in the Mnisi Traditional Authority. Together with every sample we have done a survey with questions focused on possible risk factors for the infection of Cryptosporidium. All the samples were analyzed microscopically using a Modified Ziehl Neelsen staining method and were viewed under the microscope for Cryptosporidium oocysts. In this study only 2 positives were detected out of the 354 fecal samples. Resulting in a prevalence of 0,6%. A Fisher’s exact test is used to calculate for risk factors. Unfortunately this research did not found any significant risk factors for calves to get cryptosporidiosis.

The transmission of infectious diseases of livestock does not differ in principle from disease transmission in any other animals, apart from that the aim of control is ultimately economic, with the influence of social, political and welfare constraints often poorly defined. Modelling of livestock diseases suffers simultaneously from a wealth and a lack of data. On the one hand, the ability to conduct transmission experiments, detailed within-host studies and track individual animals between geocoded locations make livestock diseases a particularly rich potential source of realistic data for illuminating biological mechanisms of transmission and conducting explicit analyses of contact networks. On the other hand, scarcity of funding, as compared to human diseases, often results in incomplete and partial data for many livestock diseases and regions of the world. In this overview of challenges in livestock disease modelling, we highlight eight areas unique to livestock that, if addressed, would mark major progress in the area.

Mycobacterium avium spp. paratuberculosis (MAP) causes a persistent infection and chronic inflammation of the gut in ruminants leading to bacterial shedding in feces in many infected animals. Although there are often strong MAP-specific immune responses in infected animals, immunological correlates of protection against progression to disease remain poorly defined. Analysis of cross-sectional data has suggested that the cellular immune response observed early in infection is effective at containing bacterial growth and shedding, in contrast to humoral immune responses. In this study, 20 MAP-infected calves were followed for nearly 5 years during which MAP shedding, antigen-specific cellular (LPT) and humoral (ELISA) immune responses were measured. We found that MAP-specific cellular immune response developed slowly, with the peak of the immune response occurring one year post infection. MAP-specific humoral immunity expanded only in a subset of animals. Only in a subset of animals there was a statistically significant negative correlation between the amount of MAP shedding and magnitude of the MAP-specific cellular immune response. Direct fitting of simple mechanistic mathematical models to the shedding data suggested that MAP-specific immune responses contributed significantly to the kinetics of MAP shedding in most animals. However, whereas the MAP-specific cellular immune response was predicted to suppress shedding in some animals, in other animals it was predicted to increase shedding. In contrast, MAP-specific humoral response was always predicted to increase shedding. Our results illustrate the use of mathematical methods to understand relationships between mycobacteria and immunity in vivo but also highlight problems with establishing cause-effect links from observational data