I am a complex-systems physicist working at the boundary between statistical physics and epidemiology.
Using mathematical modelling, I search for general principles in the dynamics of infectious diseases and
for quantitative insight into the controllability of outbreaks — with a focus on systems that adapt to
what is spreading in them: behaviour that reacts to risk, testing that reacts to prevalence, and contact
networks that rewire under both.
Proceedings of the National Academy of Sciences, 122(44), e2524944122
Laura Müller, Piklu Mallick, Antonio B. Marín-Carballo, Philipp Dönges, Robyn Kettlitz, Carolina Judith Klett-Tammen, Mirjam Kretzschmar, Viola Priesemann, Seba Contreras†
Philipp Dönges*, Joel Wagner*, Seba Contreras*, Emil N. Iftekhar*, Simon Bauer, Sebastian B. Mohr, Jonas Dehning, André Calero Valdez, Mirjam Kretzschmar, Michael Mäs, Kai Nagel, Viola Priesemann
Joel Wagner, Simon Bauer, Seba Contreras, Luk Fleddermann, Ulrich Parlitz, Viola Priesemann
Full abstract
HIV pre-exposure prophylaxis (PrEP) is transforming global HIV prevention, but its implementation coincides with observations of rising bacterial sexually transmitted infection (STI) rates among men who have sex with men, raising questions about whether PrEP is preventing one epidemic while facilitating others. To reconcile this apparent contradiction, we developed a minimal dynamical model of the simultaneous transmission of HIV and chlamydia (as an example of a curable STI). The model integrates three key mechanisms: 1) risk-mediated self-protective behavior, 2) reduction in condom use among PrEP users, and 3) PrEP-related asymptomatic STI screening. We show that these mechanisms can generate a "testing paradox:" True STI prevalence may decline while observed trends rise. This paradox emerges because increased PrEP uptake amplifies screening intensity, which can lower transmission but simultaneously inflate detection. By systematically mapping the parameter space of PrEP uptake, screening frequency, and risk perception, we identify broad and plausible conditions under which the paradox arises. Our findings reconcile conflicting epidemiological evidence and remark that the net effect of PrEP on STI dynamics depends critically on asymptomatic screening strategies. These results highlight the potential dual role of PrEP programs in reducing both HIV and bacterial STI incidence, while emphasizing the need to align screening and treatment policies to maximize benefits and minimize risks, e.g., antimicrobial resistance.
Full abstract
Pharmaceutical and non-pharmaceutical interventions (NPIs) have been crucial for controlling COVID-19. They are complemented by voluntary health-protective behavior, building a complex interplay between risk perception, behavior, and disease spread. We studied how voluntary health-protective behavior and vaccination willingness impact the long-term dynamics. We analyzed how different levels of mandatory NPIs determine how individuals use their leeway for voluntary actions. If mandatory NPIs are too weak, COVID-19 incidence will surge, implying high morbidity and mortality before individuals react; if they are too strong, one expects a rebound wave once restrictions are lifted, challenging the transition to endemicity. Conversely, moderate mandatory NPIs give individuals time and room to adapt their level of caution, mitigating disease spread effectively. When complemented with high vaccination rates, this also offers a robust way to limit the impacts of the Omicron variant of concern. Altogether, our work highlights the importance of appropriate mandatory NPIs to maximise the impact of individual voluntary actions in pandemic control.
Full abstract
Classically, endemic infectious diseases are expected to display relatively stable, predictable infection dynamics. Accordingly, basic disease models such as the susceptible-infected-recovered-susceptible model display stable endemic states or recurrent seasonal waves. However, if the human population reacts to high infection numbers by mitigating the spread of the disease, then this delayed behavioral feedback loop can generate infection waves itself, driven by periodic mitigation and subsequent relaxation. We show that such behavioral reactions, together with a seasonal effect of comparable impact, can cause complex and unpredictable infection dynamics, including Arnold tongues, coexisting attractors, and chaos. Importantly, these arise in epidemiologically relevant parameter regions where the costs associated to infections and mitigation are jointly minimized. By comparing our model to data, we find signs that COVID-19 was mitigated in a way that favored complex infection dynamics. Our results challenge the intuition that endemic disease dynamics necessarily implies predictability and seasonal waves and show the emergence of complex infection dynamics when humans optimize their reaction to increasing infection numbers.
Seba Contreras†, Philipp Dönges, Laura Müller, Piklu Mallick, Sydney Paltra, Ulrik Hvid, Robyn Kettlitz, Andreas Reitenbach, Rodrigo Amaral Lind, Maíra Aguiar, P. Bechtle, André Calero Valdez, Ronja Gronemeyer, Manuela Harries, Veronika K. Jaeger, André Karch, Carolina Judith Klett-Tammen, Peter Klimek, Mirjam E. Kretzschmar, Kai Nagel, Bjarke Frost Nielsen, Barbara Prainsack, Isabella M. Radhuber, Lone Simonsen, Kim Sneppen, Janik Suer, Viola Priesemann†
Seba Contreras*, Jonas Dehning*, Matthias Loidolt*, Johannes Zierenberg, Paul F Spitzner, Jorge Urrea-Quintero, Sebastian Mohr, Michael Wibral, Viola Priesemann
Full abstract
COVID-19 and previous pandemics have shown how diseases can disrupt, threaten, and transform daily life. Since pathogens and societies are continuously evolving, every pandemic is different. However, certain fundamental principles of disease transmission appear to hold true across different outbreaks. These "mechanisms" are grounded in natural laws or the very structure of our biology and societies. This paper compiles ten fundamental mechanisms, curated by a multidisciplinary team with backgrounds spanning public health, medicine, epidemiology, political science, mathematics, physics, and psychology. These mechanisms, although perhaps underappreciated, substantially shape how pandemics unfold and are controlled. The better we succeed in understanding these mechanisms and establishing this knowledge in our societies, the better we will be able to prepare for future pandemics and respond appropriately when they occur.
Full abstract
The traditional long-term solutions for epidemic control involve eradication or population immunity. Here, we analytically derive the existence of a third viable solution; a stable equilibrium at low case numbers, where test-trace-and-isolate policies partially compensate for local spreading events and only moderate restrictions remain necessary. In this equilibrium, daily cases stabilize around ten or fewer new infections per million people. However, stability is endangered if restrictions are relaxed or case numbers grow too high. The latter destabilization marks a tipping point beyond which the spread self-accelerates. We show that a lockdown can reestablish control and that recurring lockdowns are not necessary given sustained, moderate contact reduction. We illustrate how this strategy profits from vaccination and helps mitigate variants of concern. This strategy reduces cumulative cases (and fatalities) four times more than strategies that only avoid hospital collapse. In the long term, immunization, large-scale testing, and international coordination will further facilitate control.
Full abstract
Without a cure, vaccine, or proven long-term immunity against SARS-CoV-2, test-trace-and-isolate (TTI) strategies present a promising tool to contain its spread. For any TTI strategy, however, mitigation is challenged by pre- and asymptomatic transmission, TTI-avoiders, and undetected spreaders, which strongly contribute to “hidden” infection chains. Here, we study a semi-analytical model and identify two tipping points between controlled and uncontrolled spread; (1) the behavior-driven reproduction number of the hidden chains becomes too large to be compensated by the TTI capabilities, and (2) the number of new infections exceeds the tracing capacity. Both trigger a self-accelerating spread. We investigate how these tipping points depend on challenges like limited cooperation, missing contacts, and imperfect isolation. Our results suggest that TTI alone is insufficient to contain an otherwise unhindered spread of SARS-CoV-2, implying that complementary measures like social distancing and improved hygiene remain necessary.
Seba Contreras†, Philipp Dönges, Maciej Filiński, Joel Wagner, V. A. Bezborodov, Marcin Bodych, Barbara Pabjan, Franciszek Rakowski, Jan Pablo Burgard, Tyll Krueger, Viola Priesemann
Seba Contreras, Karen Y. Oróstica, Anamaria Daza-Sanchez, Joel Wagner, Philipp Dönges, David Medina-Ortiz, Matias Jara, Ricardo A. Verdugo, Carlos Conca, Viola Priesemann, Álvaro Olivera-Nappa
Full abstract
Metapopulation epidemic models help capture the spatial dimension of infectious disease spread by dividing heterogeneous populations into separate but interconnected communities, represented by nodes in a network. In the event of an epidemic, an important research question is, to what degree is the spatial information (i.e., regional or national) relevant for mitigation and (local) policymakers? This study investigates the impact of different levels of information on nationwide epidemic outcomes, modeling the reaction to the measured hazard as a feedback loop reducing contact rates in a metapopulation model based on ordinary differential equations (ODEs). Using COVID-19 and high-resolution mobility data for Germany of 2020 as a case study, our model revealed two markedly different regimes depending on the maximum contact reduction. In the first regime of (modest) mitigation , gradually increasing maximum contact reduction from zero to moderate levels delayed and spread out the onset of infection waves while gradually reducing the peak values. This effect was more pronounced when the contribution of regional information was low relative to national data. In the opposite suppression regime, the feedback-induced contact reduction is strong enough to extinguish local outbreaks and decrease the mean and variance of the peak day distribution, thus regional information was more important. When suppression or elimination is impossible, ensuring local epidemics are desynchronized helps to avoid hospitalization or intensive care bottlenecks by reallocating resources from less-affected areas. • Metapopulation model with dynamic contact regulation on different spatial scales. • Dynamic contact regulation yields two regimes: disease mitigation and suppression. • Modest regulation (mitigation) desynchronizes and delays the onset of epidemic waves. • High regulation (suppression) extinguishes local outbreaks but synchronizes them. • Mitigation benefits from regional data; suppression, from national data.
Full abstract
Household size impacts the spread of respiratory infectious diseases: Larger households tend to boost transmission by acquiring external infections more frequently and subsequently transmitting them back into the community. Furthermore, mandatory interventions primarily modulate contagion between households rather than within them. We developed an approach to quantify the role of household size in epidemics by separating within-household from out-household transmission, and found that household size explains 41% of the variability in cumulative COVID-19 incidence across 34 European countries (95% confidence interval: [15%, 46%]). The contribution of households to the overall dynamics can be quantified by a boost factor that increases with the effective household size, implying that countries with larger households require more stringent interventions to achieve the same levels of containment. This suggests that households constitute a structural (dis-)advantage that must be considered when designing and evaluating mitigation strategies.
Full abstract
Genomic surveillance of infectious diseases allows monitoring circulating and emerging variants and quantifying their epidemic potential. However, due to the high costs associated with genomic sequencing, only a limited number of samples can be analysed. Thus, it is critical to understand how sampling impacts the information generated. Here, we combine a compartmental model for the spread of COVID-19 (distinguishing several SARS-CoV-2 variants) with different sampling strategies to assess their impact on genomic surveillance. In particular, we compare adaptive sampling, i.e., dynamically reallocating resources between screening at points of entry and inside communities, and constant sampling, i.e., assigning fixed resources to the two locations. We show that adaptive sampling uncovers new variants up to five weeks earlier than constant sampling, significantly reducing detection delays and estimation errors. This advantage is most prominent at low sequencing rates. Although increasing the sequencing rate has a similar effect, the marginal benefits of doing so may not always justify the associated costs. Consequently, it is convenient for countries with comparatively few resources to operate at lower sequencing rates, thereby profiting the most from adaptive sampling. Finally, our methodology can be readily adapted to study undersampling in other dynamical systems.
