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Background:. Since the outbreak of coronavirus disease 2019 (COVID-19), human mobility restriction measures have raised controversies, partly because of the inconsistent findings. An empirical study is promptly needed to reliably assess the causal effects of the mobility restriction. The purpose of this study was to quantify the causal effects of human mobility restriction on the spread of COVID-19. Methods:. Our study applied the difference-in-difference (DID) model to assess the declines of population mobility at the city level, and used the log–log regression model to examine the effects of population mobility declines on the disease spread measured by cumulative or new cases of COVID-19 over time after adjusting for confounders. Results:. The DID model showed that a continual expansion of the relative declines over time in 2020. After 4 weeks, population mobility declined by −54.81% (interquartile range, −65.50% to −43.56%). The accrued population mobility declines were associated with the significant reduction of cumulative COVID-19 cases throughout 6 weeks (ie, 1% decline of population mobility was associated with 0.72% [95% CI: 0.50%–0.93%] reduction of cumulative cases for 1 week, 1.42% 2 weeks, 1.69% 3 weeks, 1.72% 4 weeks, 1.64% 5 weeks, and 1.52% 6 weeks). The impact on the weekly new cases seemed greater in the first 4 weeks but faded thereafter. The effects on cumulative cases differed by cities of different population sizes, with greater effects seen in larger cities. Conclusions:. Persistent population mobility restrictions are well deserved. Implementation of mobility restrictions in major cities with large population sizes may be even more important.

Several epidemics, such as the Black Death and the Spanish flu, have threatened human life throughout history, however, it is unclear if humans will remain safe from the sudden and fast spread of epidemic diseases. Moreover, the transmission characteristics of epidemics remain undiscovered. In this study, we present the results of an epidemic simulation experiment revealing the relationship between epidemic parameters and pandemic risk. To analyze the time-dependent risk and impact of epidemics, we considered two parameters for infectious diseases: the recovery time from infection and the transmission rate of the disease. Based on the epidemic simulation, we identified two important aspects of human safety with regard to the threat of a pandemic. First, humans should be safe if the fatality rate is below 100%. Second, even when the fatality rate is 100%, humans would be safe if the average degree of human social networks is below a threshold value. Nevertheless, certain diseases can potentially infect all nodes in the human social networks, and these diseases cause a pandemic when the average degree is larger than the threshold value. These results indicated that certain infectious diseases lead to human extinction and can be prevented by minimizing human contact.

An outbreak of foot-and mouth disease (FMD) in an FMD-free country such as Austria would likely have serious consequences for the national livestock sector and economy. The objective of this study was to analyse the epidemiological and economic impact of an FMD outbreak in Austria in order to (i) evaluate the effectiveness of different control measures in two Austrian regions with different livestock structure and density, (ii) analyse the associated costs of the control measures and the losses resulting from trade restrictions on livestock and livestock products and (iii) assess the resources that would be required to control the FMD outbreak. The European Foot-and-Mouth Disease Spread Model (EuFMDiS) was used to simulate a potential FMD outbreak. Based on the epidemiological outputs of the model, the economic impact of the outbreak was assessed. The analysis of the simulations showed that the success of control strategies depends largely on the type of control measures, the geographical location, the availability of sufficient resources, and the speed of intervention. The comparison of different control strategies suggested that from an economic point of view the implementation of additional control measures, such as pre-emptive depopulation of susceptible herds, would be efficient if the epidemic started in an area with high livestock density. Depending on the chosen control measures and the affected region, the majority of the total costs would be attributable to export losses (e.g., each day of an FMD epidemic costs Austria € 9–16 million). Our analysis indicated that the currently estimated resources for surveillance, cleaning, and disinfection during an FMD outbreak in Austria would be insufficient, which would lead to an extended epidemic control duration. We have shown that the control of an FMD outbreak can be improved by implementing a contingency strategy adapted to the affected region and by placing particular focus on an optimal resource allocation and rapid detection of the disease in Austria. The model results can assist veterinary authorities in planning resources and implementing cost-effective control measures for future outbreaks of highly contagious viral diseases.

This paper studies the interplay between the social distancing and the spread of COVID-19 disease—a widely spread pandemic that has affected nearly most of the world population. Starting in China, the virus has reached the United States of America with devastating consequences. Other countries severely affected by the pandemic are Brazil, Russia, United Kingdom, Spain, India, Italy, and France. Even though it is not possible to eliminate the spread of the virus from the world or any other country, it might be possible to reduce its effect by decreasing the number of infected people. Implementing such policies needs a good understanding of the system’s dynamics, generally not possible with mathematical linear equations or Monte Carlo methods because human society is a complex adaptive system with complex and continuous feedback loops. As a result, we use agent-based methods to conduct our study. Moreover, recent agent-based modeling studies for the COVID-19 pandemic show significant promise assisting decision-makers in managing the crisis through applying some policies such as social distancing, disease testing, contact tracing, home isolation, providing good emergency and hospitalization strategies, and preventing traveling would lead to reducing the infection rates. Based on imperial college modeling studies that prove increasing levels of interventions could slow down the spread of disease and infection cases as much as possible, and taking into account that social distancing policy is considered to be the most important factor that was recommended to follow. Our proposed model is designed to test if increasing the social distancing policies strictness can slow down the spread of disease significantly or not, and find out what is the required safe level of social distancing. So, the model was run six times, with six different percentages of social distancing with keeping the other parameters levels fixed for all experiments. The results of our study show that social distancing affects the spread of COVID-19 significantly, where the spread of disease and infection rates decrease once social distancing procedures are implemented at higher levels. Also, the behavior space tool was used to run ten experiments with different levels of social distancing, which supported the previous results. We concluded that applying and increasing social distancing policy levels led to significantly reduced infection rates, which result in decreasing deaths. Both types of experiments prove that infection rates are reduced dramatically when the level of social distancing intervention is implemented between 80% to 100%.

Understanding the geographic extent and connectivity of wildlife populations can provide important insights into the management of disease outbreaks but defining patterns of population structure is difficult for widely distributed species. Landscape genetic analyses are powerful methods for identifying cryptic structure and movement patterns that may be associated with spatial epizootic patterns in such cases.We characterized patterns of population substructure and connectivity using microsatellite genotypes from 2,222 white‐tailed deer (Odocoileus virginianus) in the Mid‐Atlantic region of the United States, a region where chronic wasting disease was first detected in 2009. The goal of this study was to evaluate the juxtaposition between population structure, landscape features that influence gene flow, and current disease management units.Clustering analyses identified four to five subpopulations in this region, the edges of which corresponded to ecophysiographic provinces. Subpopulations were further partitioned into 11 clusters with subtle (F ST ≤ 0.041), but significant genetic differentiation. Genetic differentiation was lower and migration rates were higher among neighboring genetic clusters, indicating an underlying genetic cline. Genetic discontinuities were associated with topographic barriers, however.Resistance surface modeling indicated that gene flow was diffuse in homogenous landscapes, but the direction and extent of gene flow were influenced by forest cover, traffic volume, and elevational relief in subregions heterogeneous for these landscape features. Chronic wasting disease primarily occurred among genetic clusters within a single subpopulation and along corridors of high landscape connectivity.These results may suggest a possible correlation between population substructure, landscape connectivity, and the occurrence of diseases for widespread species. Considering these factors may be useful in delineating effective management units, although only the largest features produced appreciable differences in subpopulation structure. Disease mitigation strategies implemented at the scale of ecophysiographic provinces are likely to be more effective than those implemented at finer scales., We characterized the spatial population structure of white‐tailed deer in the Mid‐Atlantic region of the United States in order to evaluate the juxtaposition population structure, gene flow patterns, and chronic wasting disease cases in the region. We found evidence of four to five subpopulation units, the extents of which generally corresponded with ecophysiographic provinces, with 11 subtle genetic clusters nested hierarchically within subpopulation units. Landscape genetic analyses revealed corridors of high connectivity adjacent to ridges, roads, and open areas, which may indicate directed disease transmission in regions heterogeneous for these features.

International audience; 1. Ash dieback is induced by Hymenoscyphus fraxineus, an invasive pathogenic fungus. It is causing severe damage to European ash populations. However, the local environment, such as climate or site conditions, is known to affects ash dieback.2. We studied the landscape epidemiology of the disease on a 22 km² area in northeastern France at two stages of the invasion process using Bayesian spatio-temporal models fitted with integrated nested Laplace approximation (INLA). Several features characterizing disease severity, crown dieback, frequency of collar canker, and density of infected leaf debris in the litter were determined on a regular grid over a 3.5 x 6.5 km area. We first analysed the effect of landscape features on the disease establishment stage in 2012, two years after the first report of the disease in the area, and then on further disease development, in 2016-2018. 3.Landscape features had little impact on the disease at the establishment stage, but strongly determined its further development. Local fragmentation of tree cover was the most important factor, with trees that are isolated or in hedges far less affected than trees in a forest environment. We showed that they were subjected to different microclimates, with higher crown temperatures unfavourable to pathogen development. Low host density strongly reduced disease development. The presence of large ash populations in the vicinity affected local disease severity up to several hundred meters. 4. Synthesis. We showed that the landscape characteristics strongly affect the development and spread of ash dieback. The disease is far less severe in forest conditions when ash density is low or in open canopies such as hedges and isolated trees. Ash trees are often in these types of landscapes, which should strongly limit the overall impact of Ash dieback.

Simple Summary Genetic diversity in wildlife is a matter of growing concern in contexts related to disease transmission and human health. Tuberculosis is a zoonotic disease with relevant consequences and can present high prevalence in wild boar and red deer populations. Here, we review studies on the genetic diversity of ungulates, wild boar, and red deer, and assess to what extent these studies consider its importance in the spread of disease. The relationship between host genetic diversity and the probability of disease spread is illustrated in Spanish populations of wild boar and red deer. Abstract Host genetic diversity tends to limit disease spread in nature and buffers populations against epidemics. Genetic diversity in wildlife is expected to receive increasing attention in contexts related to disease transmission and human health. Ungulates such as wild boar (Sus scrofa) and red deer (Cervus elaphus) are important zoonotic hosts that can be precursors to disease emergence and spread in humans. Tuberculosis is a zoonotic disease with relevant consequences and can present high prevalence in wild boar and red deer populations. Here, we review studies on the genetic diversity of ungulates and determine to what extent these studies consider its importance on the spread of disease. This assessment also focused on wild boar, red deer, and tuberculosis. We found a disconnection between studies treating genetic diversity and those dealing with infectious diseases. Contrarily, genetic diversity studies in ungulates are mainly concerned with conservation. Despite the existing disconnection between studies on genetic diversity and studies on disease emergence and spread, the knowledge gathered in each discipline can be applied to the other. The bidirectional applications are illustrated in wild boar and red deer populations from Spain, where TB is an important threat for wildlife, livestock, and humans.

OBJECTIVE: This study aimed to investigate the anatomic pattern of disease spread at first disease relapse compared with baseline in diffuse large B-cell lymphoma (DLBCL). MATERIALS AND METHODS: All patients who were newly diagnosed as having DLBCL between January 2004 and June 2014 who initially achieved complete remission but who eventually developed relapsed disease during follow-up were retrospectively identified. Available histological and imaging data were used to determine which nodal regions and extranodal locations were involved at relapse. RESULTS: A total of 21 patients with relapsed DLBCL were included, of whom 8 (38.1%) presented with disease relapse at previously involved sites only, 7 (33.3%) presented with disease relapse at both previously involved and new sites, and 6 (28.6%) presented with disease relapse at new sites only. A total of 57 nodal stations and 34 extranodal locations were involved in all 21 relapsed DLBCL patients. Of these 57 involved nodal regions, 47 (82.5%) were also involved at baseline, whereas 10 (17.5%) were not involved at baseline. Of the 34 involved extranodal locations, 17 (50.0%) were also involved at baseline, whereas 17 (50.0%) were not involved at baseline. CONCLUSIONS: Relapsed DLBCL generally tends to affect previously involved sites, although close to one third of patients seem to have disease recurrence exclusively in previously uninvolved sites. The great majority of involved nodal stations at relapse are also involved at baseline, whereas only one half of involved extranodal locations at relapse are involved at baseline.

Background and Aims: Two types of epithelial ovarian carcinoma (EOC) have been recently distinguished. Type I comprises low-grade serous, endometrioid and clear-cell tumors. High-grade endometrioid and serous tumors belong to type II. The aim of this study was to compare patterns of disease spread in advanced-stage type I and II EOCs at primary surgery. Methods: Surgical and pathological data of 233 patients with advanced-stage EOCs were collected, 42 with type I and 191 with type II. The two groups were compared for tumor localization at primary surgery. Intraoperative mapping of ovarian cancer (IMO) was used to assess tumor dissemination. Results: Tumor involvement was significantly higher in the type II group for the following: peritoneum (68.1 vs. 40.5%, p < 0.001), pouch of Douglas (60.2 vs. 40.5%, p = 0.06), vesicouterine ligament (40.8 vs. 19%, p = 0.027), diaphragm (45.0 vs. 11.9%, p < 0.001), serosa of liver (17.2 vs. 4.8%, p = 0.05), omentum (81.1 vs. 59.5%, p = 0.007), mesentery (42.9 vs. 16.7%, p = 0.005), pleural effusions (19.4 vs. 4.6%, p = 0.01) and ascites (60.7 vs. 21.4%, p < 0.001). IMO levels were different between the two groups (p = 0.001). Conclusions: This study provides clinical evidence in favor of the dualistic model of carcinogenesis, since types I and II are characterized by different findings at primary surgery.

In 2012, an outbreak of infection with Middle East respiratory syndrome coronavirus (MERS-CoV), was detected in the Arabian Peninsula. Modeling can produce estimates of the expected annual number of symptomatic cases of MERS-CoV infection exported and the likelihood of exportation from source countries in the Middle East to countries outside the region.

The highly pathogenic avian influenza (HPAI) strain H5N1, which first appeared in Hong Kong in 1997, achieved bird-to-human transmission, causing a severe disease with high mortality to humans [18] . According to the World Health Organization (WHO), a total of 637 cases were reported in fifteen countries, including 378 deaths, corresponding to a case fatality rate of nearly 60% [19] . Avian influenza continues to be one of the deadliest diseases that jumps from animals to humans. Epidemiologists believe that it is likely to cause the next major global pandemic that could kill millions of people. The 2002 outbreak of severe acute respiratory syndrome (SARS) demonstrated that international air travel can significantly influence the global spread of an infectious disease. This paper studies the effects of air travel on the spread of avian influenza from Asian and Australian cities to the United States. A two-city mathematical model involving a pandemic strain is used to derive the basic reproduction number ( R 0 ), which determines if the disease will spread and persist ( R 0 > 1 ) or go extinct ( R 0 1 ). Real air travel data is used to model the disease spread by individuals who are susceptible to or are infected with pandemic avian influenza. Analysis of the two-city model helps understand the dynamics of the spread of pandemic influenza when the cities are connected by air travel. Understanding these effects can help public health officials and policy-makers select the appropriate disease control measures. Also, it can provide guidance to decision-makers on where to implement control measures while conserving precious resources.

Understanding risk factors for the spread of infectious diseases over time and across the landscape is critical for managing disease risk. While habitat connectivity and characteristics of local and neighboring animal (i.e., host) assemblages are known to influence the spread of diseases, the interactions among these factors remain poorly understood. In this study, we conducted a county-level analysis to test the effects of forest connectivity, together with the suitability of local assemblage (measured by the similarity of local host assemblage with neighboring assemblages) and the infection intensity of neighboring counties on the spatial expansion of Lyme disease in the United States. Our results suggested that both the similarity of local host assemblage and the infection intensity of neighboring counties were positively correlated with the probability of disease spread. Moreover, we found that increasing forest connectivity could facilitate the positive effect of neighbor infection intensity. In contrast, the effect size of the host assemblage similarity decreased with increasing connectivity, suggesting that host assemblage similarity was less effective in well-connected habitats. Our results thus indicate that habitat connectivity can indirectly influence disease spread by mediating the effects of other risk factors.

Since the introduction of African swine fever (ASF) into Georgia in 2007, the disease has been spreading in an unprecedented way. Many countries that are still free from the disease fear the emergence of ASF in their territory either in domestic pigs or in wild boar. In the past, ASF was often described as being a highly contagious disease with mortality often up to 100%. However, the belief that the disease might enter a naïve population and rapidly affect the entire susceptible population needs to be critically reviewed. The current ASF epidemic in wild boar, but also the course of ASF within outbreaks in domestic pig holdings, suggest a constant, but relatively slow spread. Moreover, the results of several experimental and field studies support the impression that the spread of ASF is not always fast. ASF spread and its speed depend on various factors concerning the host, the virus, and also the environment. Many of these factors and their effects are not fully understood. For this review, we collated published information regarding the spreading speed of ASF and the factors that are deemed to influence the speed of ASF spread and tried to clarify some issues and open questions in this respect.

A comprehensive monitoring strategy is vital for tracking the spread of mosquito-borne Japanese encephalitis virus (JEV), the leading cause of viral encephalitis in Asia. Virus detection consists of passive surveillance of primarily humans and swine, and/or active surveillance in mosquitoes, which may be a valuable proxy in providing insights into ecological processes underlying the spread and persistence of JEV. However, it has not been well characterized whether passive surveillance alone can capture the circulating genetic diversity to make reasonable inferences. Here, we develop phylogenetic models to infer JEV host changes, spatial diffusion patterns, and evolutionary dynamics from data collected through active and passive surveillance. We evaluate the feasibility of using JEV sequence data collected from mosquitoes to estimate the migration histories of genotypes GI and GIII. We show that divergence times estimated from this dataset were comparable to estimates from all available data. Increasing the amount of data collected from active surveillance improved time of most recent common ancestor estimates and reduced uncertainty. Phylogenetic estimates using all available data and only mosquito data from active surveillance produced similar results, showing that GI epidemics were widespread and diffused significantly faster between regions than GIII. In contrast, GIII outbreaks were highly structured and unlinked suggesting localized, unsampled infectious sources. Our results show that active surveillance of mosquitoes can sufficiently capture circulating genetic diversity of JEV to confidently estimate spatial and evolutionary patterns. While surveillance of other hosts could contribute to more detailed disease tracking and evaluation, comprehensive JEV surveillance programs should include systematic surveillance in mosquitoes to infer the most complete patterns for epidemiology, and risk assessment.

The emergence and global spread of human infectious diseases has become one of the most serious public health threats of the 21st century. Sophisticated computer simulations have become a key tool for understanding and predicting disease spread on a global scale. Combining theoretical insights from nonlinear dynamics, stochastic processes and complex network theory these computational models are becoming increasingly important in the design of efficient mitigation and control strategies and for public health in general.

Producción Científica, The fungal pathogen Fusarium circinatum is the causal agent of Pine Pitch Canker (PPC), a disease which seriously affects different species of pine in forests and nurseries worldwide. In Europe, the fungus affects pines in northern Spain and Portugal, and it has also been detected in France and Italy. Here, we report the findings of the first trial investigating the susceptibility of Polish provenances of Scots pine, Pinus sylvestris L., to infection by F. circinatum. In a greenhouse experiment, 16 Polish provenances of Scots pine were artificially inoculated with F. circinatum and with six other Fusarium species known to infect pine seedlings in nurseries. All pines proved highly susceptible to PPC and displayed different levels of susceptibility to the other Fusarium spp. tested. The findings obtained indicate the potentially strong threat of establishment of an invasive pathogen such as F. circinatum following unintentional introduction into Poland., Fund of the Forest Research Institute (Poland) through Grant No. 240327, Ministry of Education and Science of Ukraine within joint Ukrainian-Lithuanian project No. M/93-2018, Portuguese Foundation for Science and Technology (FCT) (grant-SFRH/BPD/122928/2016)

Chagas disease is caused by the protozoan Trypanosoma cruzi. This is an endemic disease in the Americas, but increased migration to Europe has made it emerge in countries where it was previously unknown, being Spain the second non endemic country in number of patients. T. cruzi is a parasite with a wide genetic diversity, which has been grouped by consensus into 6 Discrete Typing Units (DTUs) affecting humans. Some authors have linked these DTUs either to a specific epidemiological context or to the different clinical presentations. Our main objective was to describe the T. cruzi DTUs identified from a population of chronically infected Latin American migrants attending a reference clinic in Madrid. 149 patients meeting this condition were selected for the study. Molecular characterization was performed by an algorithm that combines PCR of the intergenic region of the mini exon-gene, the 24S? and 18S regions of rDNA and the variable region of the satellite DNA. A descriptive analysis was performed and associations between geographical/clinical data and the different DTUs were tested. DTUs could be determined in 105 out of 149 patients, 93.3% were from Bolivia, 67.7% were women and median age was 35 years (IQR 29-44). The most common DTU found was TcV (58; 55.2%), followed by TcIV (17; 16.2%), TcII (10; 9.5%) and TcI (4; 3.8%). TcIII and TcVI were not identified from any patient, and 15.2% patients presented mixed infections. In addition, we determined DTUs after treatment in a subset of patients. In 57% patients had different DTUs before and after treatment. DTUs distribution from this study indicates active transmission of T. cruzi is occurring in Bolivia, in both domestic and sylvatic cycles. TcIV was confirmed as a cause of chronic human disease. The current results indicate no correlation between DTU and any specific clinical presentation associated with Chagas disease, nor with geographical origin. Treatment with benznidazole does not always clear T. cruzi's genetic material from blood, and DTUs detected in the same patient may vary over time indicating that polyparasitism is frequent. © 2016 Elsevier B.V.

Like many countries in Sub-Saharan Africa, Hepatitis B virus (HBV) is highly prevalent in Ghana. Using qualitative methods, this paper draws from the political ecology of health theoretical framework to examine perceptions and understandings of HBV in the Upper West Region of Ghana. The findings reveal that extremely low levels of knowledge and pervasive lay misconceptions about the disease within this geographic context are shaped by large scale structural influences. Furthermore, in this context there is essentially no access to HBV immunizations, testing or treatment services which reinforces potential routes for the spread of HBV. An explosive spread of HBV is brewing with the potential to diffuse across space and time while, within the institutional contexts, it is the HIV epidemic that is largely consuming both policy attention and intervention.

We have studied the spread of epidemics characterized by a mixture of local and non-local interactions. The infection spreads on a two-dimensional lattice with the fixed nearest neighbour connections. In addition, long-range dynamical links are formed by moving agents (vectors). Vectors perform random walks, with step length distributed according to a thick-tail distribution. Two distributions are considered in this paper, an α -stable distribution describing self-similar vector movement, yet characterized by an infinite variance and an exponential power characterized by a large but finite variance. Such long-range interactions are hard to track and make control of epidemics very difficult. We also allowed for cryptic infection, whereby an infected individual on the lattice can be infectious prior to showing any symptoms of infection or disease. To account for such cryptic spread, we considered a control strategy in which not only detected, i.e. symptomatic, individuals but also all individuals within a certain control neighbourhood are treated upon the detection of disease. We show that it is possible to eradicate the disease by using such purely local control measures, even in the presence of long-range jumps. In particular, we show that the success of local control and the choice of the optimal strategy depend in a non-trivial way on the dispersal patterns of the vectors. By characterizing these patterns using the stability index of the α -stable distribution to change the power-law behaviour or the exponent characterizing the decay of an exponential power distribution, we show that infection can be successfully contained using relatively small control neighbourhoods for two limiting cases for long-distance dispersal and for vectors that are much more limited in their dispersal range.

Characterizing spatio-temporal patterns among epidemics in which the mechanism of spread is uncertain is important for generating disease spread hypotheses, which may in turn inform disease control and prevention strategies. Using a dataset representing three phases of highly pathogenic avian influenza H5N1 outbreaks in village poultry in Romania, 2005� 2006, spatio- temporal patterns were characterized. We first fit a set of hierarchical Bayesian models that quantified changes in the spatio-temporal relative risk for each of the 23 affected counties. We then modeled spatial synchrony in each of the three epidemic phases using non-parametric covariance functions and Thin Plate Spline regression models. We found clear differences in the spatio-temporal patterns among the epidemic phases (local versus regional correlated processes), which may indicate differing spread mechanisms (for example wild bird versus human-mediated). Elucidating these patterns allowed us to postulate that a shift in the primary mechanism of disease spread may have taken place between the second and third phases of this epidemic. Information generated by such analyses could assist affected countries in determining the most appropriate control programs to implement, and to allocate appropriate resources to preventing contact between domestic poultry and wild birds versus enforcing bans on poultry movements and quarantine. The methods used in this study could be applied in many different situations to analyze transboundary disease data in which only location and time of occurrence data are reported. disease spread / spatio-temporal analysis / epidemic pattern / avian influenza / poultry

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, characterized by progressive loss of motor function. While the pathogenesis of ALS remains largely unknown, recent histological examinations of Brettschneider and colleagues have proposed four time-sequential stages of neuropathology in ALS based on levels of phosphorylated 43. kDa TAR DNA-binding protein (pTDP-43) aggregation. What governs dissemination of these aggregates between segregated regions of the brain is unknown. Here, we cross-reference stages of pTDP-43 pathology with in vivo diffusion weighted imaging data of 215 adult healthy control subjects, and reveal that regions involved in pTDP-43 pathology form a strongly interconnected component of the brain network (p = 0.04) likely serving as an anatomical infrastructure facilitating pTDP-43 spread. Furthermore, brain regions of subsequent stages of neuropathology are shown to be more closely interconnected than regions of more distant stages (p = 0.002). Computational simulation of disease spread from first-stage motor regions across the connections of the brain network reveals a pattern of pTDP-43 aggregation that reflects the stages of sequential involvement in neuropathology (p = 0.02), a pattern in favor of the hypothesis of pTDP-43 pathology to spread across the brain along axonal pathways. Our findings thus provide computational evidence of disease spread in ALS to be directed and constrained by the topology of the anatomical brain network.