Christiaan H. van Dorp

Bio: Christiaan H. van Dorp is an academic researcher from Los Alamos National Laboratory. The author has contributed to research in topics: Medicine & Selection (genetic algorithm). The author has an hindex of 6, co-authored 19 publications receiving 127 citations. Previous affiliations of Christiaan H. van Dorp include Utrecht University & Government of the United States of America.

Controlling the pandemic during the SARS-CoV-2 vaccination rollout.

Abstract: There is a consensus that mass vaccination against SARS-CoV-2 will ultimately end the COVID-19 pandemic. However, it is not clear when and which control measures can be relaxed during the rollout of vaccination programmes. We investigate relaxation scenarios using an age-structured transmission model that has been fitted to age-specific seroprevalence data, hospital admissions, and projected vaccination coverage for Portugal. Our analyses suggest that the pressing need to restart socioeconomic activities could lead to new pandemic waves, and that substantial control efforts prove necessary throughout 2021. Using knowledge on control measures introduced in 2020, we anticipate that relaxing measures completely or to the extent as in autumn 2020 could launch a wave starting in April 2021. Additional waves could be prevented altogether if measures are relaxed as in summer 2020 or in a step-wise manner throughout 2021. We discuss at which point the control of COVID-19 would be achieved for each scenario. Despite the consensus that mass vaccination against SARS-CoV-2 will ultimately end the pandemic, it is not clear when and which control measures can be relaxed during the rollout of vaccination programmes. Here, the authors investigate relaxation scenarios using an age-structured transmission model that has been fitted to data for Portugal.

Controlling the pandemic during the SARS-CoV-2 vaccination rollout: a modeling study

Abstract: There is a consensus that mass vaccination against SARS-CoV-2 will ultimately end the COVID-19 pandemic. However, it is not clear when and which control measures can be relaxed during the rollout of vaccination programmes. We investigate relaxation scenarios using an age-structured transmission model that has been fitted to age-specific seroprevalence data, hospital admissions, and projected vaccination coverage for Portugal. Our analyses suggest that the pressing need to restart socioeconomic activities could lead to new pandemic waves, and that substantial control efforts prove necessary throughout 2021. Using knowledge on control measures introduced in 2020, we anticipate that relaxing measures completely or to the extent as in autumn 2020 could launch a wave starting in April 2021. Additional waves could be prevented altogether if measures are relaxed as in summer 2020 or in a step-wise manner throughout 2021. We discuss at which point control of COVID-19 would be achieved for each scenario.

Model-based evaluation of school- and non-school-related measures to control the COVID-19 pandemic.

Abstract: The role of school-based contacts in the epidemiology of SARS-CoV-2 is incompletely understood. We use an age-structured transmission model fitted to age-specific seroprevalence and hospital admission data to assess the effects of school-based measures at different time points during the COVID-19 pandemic in the Netherlands. Our analyses suggest that the impact of measures reducing school-based contacts depends on the remaining opportunities to reduce non-school-based contacts. If opportunities to reduce the effective reproduction number (Re) with non-school-based measures are exhausted or undesired and Re is still close to 1, the additional benefit of school-based measures may be considerable, particularly among older school children. As two examples, we demonstrate that keeping schools closed after the summer holidays in 2020, in the absence of other measures, would not have prevented the second pandemic wave in autumn 2020 but closing schools in November 2020 could have reduced Re below 1, with unchanged non-school-based contacts.

Infectious reactivation of cytomegalovirus explaining age- and sex-specific patterns of seroprevalence

Abstract: Human cytomegalovirus (CMV) is a herpes virus with poorly understood transmission dynamics. Person-to-person transmission is thought to occur primarily through transfer of saliva or urine, but no quantitative estimates are available for the contribution of different infection routes. Using data from a large population-based serological study (n = 5,179), we provide quantitative estimates of key epidemiological parameters, including the transmissibility of primary infection, reactivation, and re-infection. Mixture models are fitted to age- and sex-specific antibody response data from the Netherlands, showing that the data can be described by a model with three distributions of antibody measurements, i.e. uninfected, infected, and infected with increased antibody concentration. Estimates of seroprevalence increase gradually with age, such that at 80 years 73% (95%CrI: 64%-78%) of females and 62% (95%CrI: 55%-68%) of males are infected, while 57% (95%CrI: 47%-67%) of females and 37% (95%CrI: 28%-46%) of males have increased antibody concentration. Merging the statistical analyses with transmission models, we find that models with infectious reactivation (i.e. reactivation that can lead to the virus being transmitted to a novel host) fit the data significantly better than models without infectious reactivation. Estimated reactivation rates increase from low values in children to 2%-4% per year in women older than 50 years. The results advance a hypothesis in which transmission from adults after infectious reactivation is a key driver of transmission. We discuss the implications for control strategies aimed at reducing CMV infection in vulnerable groups.

Immuno-epidemiological modeling of HIV-1 predicts high heritability of the set-point virus load, while selection for CTL escape dominates virulence evolution.

Abstract: It has been suggested that HIV-1 has evolved its set-point virus load to be optimized for transmission. Previous epidemiological models and studies into the heritability of set-point virus load confirm that this mode of adaptation within the human population is feasible. However, during the many cycles of replication between infection of a host and transmission to the next host, HIV-1 is under selection for escape from immune responses, and not transmission. Here we investigate with computational and mathematical models how these two levels of selection, within-host and between-host, are intertwined. We find that when the rate of immune escape is comparable to what has been observed in patients, immune selection within hosts is dominant over selection for transmission. Surprisingly, we do find high values for set-point virus load heritability, and argue that high heritability estimates can be caused by the ‘footprints’ left by differing hosts' immune systems on the virus.

Genetic Selection Bias at the Heterosexual HIV-1 Transmission Bottleneck

Abstract: Introduction Heterosexual HIV-1 transmission is an inefficient process with rates reported at <1% per unprotected sexual exposure. When transmission occurs, systemic infection is typically established by a single genetic variant, taken from the swarm of genetically distinct viruses circulating in the donor. Whether that founder virus represents a chance event or was systematically favored is unclear. Our work has tested a central hypothesis that founder virus selection is biased toward certain genetic characteristics. Fitter viruses (red) are favored more in woman-to-man (bottom curve) than in man-to-woman (top curve) transmission. The probability that a majority donor amino acid variant is transmitted is a function of relative fitness, here estimated by the frequency of the variant in the Zambian population. Even residues common in the population are less likely to be transmitted to healthy men than to women, indicative of higher selection bias in woman-to-man transmission. Rationale If HIV-1 transmission involves selection for viruses with certain favorable characteristics, then such advantages should emerge as statistical biases when viewed across many viral loci in many transmitting partners. We therefore identified 137 Zambian heterosexual transmission pairs, for whom plasma samples were available for both the donor and recipient partner soon after transmission, and compared the viral sequences obtained from each partner to identify features that predicted whether the majority amino acid observed at any particular position in the donor was transmitted. We focused attention on two features: viral genetic characteristics that correlate with viral fitness, and clinical factors that influence transmission. Statistical modeling indicates that the former will be favored for transmission, while the latter will nullify this relative advantage. Results We observed a highly significant selection bias that favors the transmission of amino acids associated with increased fitness. These features included the frequency of the amino acid in the study cohort, the relative advantage of the amino acid with respect to the stability of the protein, and features related to immune escape and compensation. This selection bias was reduced in couples with high risk of transmission. In particular, significantly less selection bias was observed in men with genital inflammation and in women (regardless of inflammation status), compared to healthy men, suggesting a more permissive environment in the female than male genital tract. Consistent with this observation, viruses transmitted to women were characterized by lower predicted fitness than those in men. The presence of amino acids favored during transmission predicted which individual virus within a donor was transmitted to their partner, while chronically infected individuals with viral populations characterized by a predominance of these amino acids were more likely to transmit to their partners. Conclusion These data highlight the clear selection biases that benefit fitter viruses during transmission in the context of a stochastic process. That such biases exist, and are tempered by certain risk factors, suggests that transmission is frequently characterized by many abortive transmission events in which some target cells are nonproductively infected. Moreover, for efficient transmission, some changes that favored survival in the transmitting partner are frequently discarded, resulting in overall slower evolution of HIV-1 in the population. Paradoxically, by increasing the selection bias at the transmission bottleneck, reduction of susceptibility may increase the expected fitness of breakthrough viruses that establish infection and may therefore worsen the prognosis for the newly infected partner. Conversely, preventive or therapeutic approaches that weaken the virus may reduce overall transmission rates via a mechanism that is independent from the quantity of circulating virus, and may therefore provide long-term benefits to the recipient if transmission does occur. HIV needs to be fit to transmit Although you might not think it, it's hard to catch HIV. Less than 1% of unprotected sexual exposures result in infection. What then leads to transmission? Carlson et al. determined the amino acid sequence of viruses infecting 137 Zambian heterosexual couples in which one partner infected the other (see the Perspective by Joseph and Swanstrom). The authors then used statistical modeling and found that transmitted viruses are typically the most evolutionarily fit. That is, compared to other viral variants in the infected person, the transmitted virus most closely matches the most common viral sequence found in the Zambian population. Science, this issue 10.1126/science.1254031; see also p. 136 An analysis of discordant couples reveals that transmitted HIV-1 viruses are typically the most evolutionarily fit. [Also see Perspective by Joseph and Swanstrom] Heterosexual transmission of HIV-1 typically results in one genetic variant establishing systemic infection. We compared, for 137 linked transmission pairs, the amino acid sequences encoded by non-envelope genes of viruses in both partners and demonstrate a selection bias for transmission of residues that are predicted to confer increased in vivo fitness on viruses in the newly infected, immunologically naïve recipient. Although tempered by transmission risk factors, such as donor viral load, genital inflammation, and recipient gender, this selection bias provides an overall transmission advantage for viral quasispecies that are dominated by viruses with high in vivo fitness. Thus, preventative or therapeutic approaches that even marginally reduce viral fitness may lower the overall transmission rates and offer long-term benefits even upon successful transmission.

Evolution of Influenza A Virus by Mutation and Re-Assortment

Abstract: Influenza A virus (IAV), a highly infectious respiratory pathogen, has continued to be a significant threat to global public health. To complete their life cycle, influenza viruses have evolved multiple strategies to interact with a host. A large number of studies have revealed that the evolution of influenza A virus is mainly mediated through the mutation of the virus itself and the re-assortment of viral genomes derived from various strains. The evolution of influenza A virus through these mechanisms causes worldwide annual epidemics and occasional pandemics. Importantly, influenza A virus can evolve from an animal infected pathogen to a human infected pathogen. The highly pathogenic influenza virus has resulted in stupendous economic losses due to its morbidity and mortality both in human and animals. Influenza viruses fall into a category of viruses that can cause zoonotic infection with stable adaptation to human, leading to sustained horizontal transmission. The rapid mutations of influenza A virus result in the loss of vaccine optimal efficacy, and challenge the complete eradication of the virus. In this review, we highlight the current understanding of influenza A virus evolution caused by the mutation and re-assortment of viral genomes. In addition, we discuss the specific mechanisms by which the virus evolves.