Willem G. van Panhuis

Contagious Diseases in the United States from 1888 to the Present

Using data from digitized weekly surveillance reports of notifiable diseases for U.S. cities and states for 1888 through 2011, the authors derived a quantitative history of disease reduction in the United States, focusing particularly on the effects of vaccination programs.

A systematic review of barriers to data sharing in public health

BackgroundIn the current information age, the use of data has become essential for decision making in public health at the local, national, and global level. Despite a global commitment to the use and sharing of public health data, this can be challenging in reality. No systematic framework or global operational guidelines have been created for data sharing in public health. Barriers at different levels have limited data sharing but have only been anecdotally discussed or in the context of specific case studies. Incomplete systematic evidence on the scope and variety of these barriers has limited opportunities to maximize the value and use of public health data for science and policy.MethodsWe conducted a systematic literature review of potential barriers to public health data sharing. Documents that described barriers to sharing of routinely collected public health data were eligible for inclusion and reviewed independently by a team of experts. We grouped identified barriers in a taxonomy for a focused international dialogue on solutions.ResultsTwenty potential barriers were identified and classified in six categories: technical, motivational, economic, political, legal and ethical. The first three categories are deeply rooted in well-known challenges of health information systems for which structural solutions have yet to be found; the last three have solutions that lie in an international dialogue aimed at generating consensus on policies and instruments for data sharing.ConclusionsThe simultaneous effect of multiple interacting barriers ranging from technical to intangible issues has greatly complicated advances in public health data sharing. A systematic framework of barriers to data sharing in public health will be essential to accelerate the use of valuable information for the global good.

Impact of changing the measles vaccine vial size on Niger's vaccine supply chain: a computational model

BackgroundMany countries, such as Niger, are considering changing their vaccine vial size presentation and may want to evaluate the subsequent impact on their supply chains, the series of steps required to get vaccines from their manufacturers to patients. The measles vaccine is particularly important in Niger, a country prone to measles outbreaks.MethodsWe developed a detailed discrete event simulation model of the vaccine supply chain representing every vaccine, storage location, refrigerator, freezer, and transport device (e.g., cold trucks, 4 × 4 trucks, and vaccine carriers) in the Niger Expanded Programme on Immunization (EPI). Experiments simulated the impact of replacing the 10-dose measles vial size with 5-dose, 2-dose and 1-dose vial sizes.ResultsSwitching from the 10-dose to the 5-dose, 2-dose and 1-dose vial sizes decreased the average availability of EPI vaccines for arriving patients from 83% to 82%, 81% and 78%, respectively for a 100% target population size. The switches also changed transport vehicles utilization from a mean of 58% (range: 4-164%) to means of 59% (range: 4-164%), 62% (range: 4-175%), and 67% (range: 5-192%), respectively, between the regional and district stores, and from a mean of 160% (range: 83-300%) to means of 161% (range: 82-322%), 175% (range: 78-344%), and 198% (range: 88-402%), respectively, between the district to integrated health centres (IHC). The switch also changed district level storage utilization from a mean of 65% to means of 64%, 66% and 68% (range for all scenarios: 3-100%). Finally, accounting for vaccine administration, wastage, and disposal, replacing the 10-dose vial with the 5 or 1-dose vials would increase the cost per immunized patient from

Inferring the Serotype Associated with Dengue Virus Infections on the Basis of Pre- and Postinfection Neutralizing Antibody Titers

0.47US to

FUNNEL: automatic mining of spatially coevolving epidemics

0.71US and

Impact of introducing the pneumococcal and rotavirus vaccines into the routine immunization program in Niger

1.26US, respectively.ConclusionsThe switch from the 10-dose measles vaccines to smaller vial sizes could overwhelm the capacities of many storage facilities and transport vehicles as well as increase the cost per vaccinated child.

Region-wide synchrony and traveling waves of dengue across eight countries in Southeast Asia

BACKGROUND Currently, the only tests capable of determining the serotype associated with dengue virus (DENV) infection require sampling during the period of acute viremia. No test can accurately detect the serotype associated with past DENV infections. The standard assay for determination of serotype-specific antibody against DENV is the plaque reduction neutralization test (PRNT), although performance of this test continues to be evaluated. METHODS From a cohort study among schoolchildren in Thailand, PRNT values were determined in serum samples collected before and after infection. A multinomial logistic regression model was used to infer the serotype associated with intercurrent DENV infections. Models were validated based on polymerase chain reaction identification of DENV serotypes. RESULTS The serotype associated with DENV infection inferred by the model corresponded with polymerase chain reaction in 67.6% of cases, and the kappa statistic was 0.479. A model for 35 cases with primary seroconversion correctly identified the DENV serotypes causing infection in 77.1% of cases, compared with 66.9%, using a model for 169 cases with secondary seroconversion. The best model using only postinfection PRNT values correctly inferred the DENV serotype causing infection in 60.3% of cases. CONCLUSIONS A statistical model based on both pre- and postinfection PRNT values can be used to infer the serotype associated with DENV infections in prospective studies and vaccine trials.

Health impact of the 2004 Andaman nicobar earthquake and tsunami in Indonesia.

Given a large collection of epidemiological data consisting of the count of d contagious diseases for l locations of duration n, how can we find patterns, rules and outliers? For example, the Project Tycho provides open access to the count infections for U.S. states from 1888 to 2013, for 56 contagious diseases (e.g., measles, influenza), which include missing values, possible recording errors, sudden spikes (or dives) of infections, etc. So how can we find a combined model, for all these diseases, locations, and time-ticks? In this paper, we present FUNNEL, a unifying analytical model for large scale epidemiological data, as well as a novel fitting algorithm, FUNNELFIT, which solves the above problem. Our method has the following properties: (a) Sense-making: it detects important patterns of epidemics, such as periodicities, the appearance of vaccines, external shock events, and more; (b) Parameter-free: our modeling framework frees the user from providing parameter values; (c) Scalable: FUNNELFIT is carefully designed to be linear on the input size; (d) General: our model is general and practical, which can be applied to various types of epidemics, including computer-virus propagation, as well as human diseases. Extensive experiments on real data demonstrate that FUNNELFIT does indeed discover important properties of epidemics: (P1) disease seasonality, e.g., influenza spikes in January, Lyme disease spikes in July and the absence of yearly periodicity for gonorrhea; (P2) disease reduction effect, e.g., the appearance of vaccines; (P3) local/state-level sensitivity, e.g., many measles cases in NY; (P4) external shock events, e.g., historical flu pandemics; (P5) detect incongruous values, i.e., data reporting errors.

The importance of conflict-related mortality in civilian populations.

OBJECTIVES We investigated whether introducing the rotavirus and pneumococcal vaccines, which are greatly needed in West Africa, would overwhelm existing supply chains (i.e., the series of steps required to get a vaccine from the manufacturers to the target population) in Niger. METHODS As part of the Bill and Melinda Gates Foundation-funded Vaccine Modeling Initiative, we developed a computational model to determine the impact of introducing these new vaccines to Nigers Expanded Program on Immunization vaccine supply chain. RESULTS Introducing either the rotavirus vaccine or the 7-valent pneumococcal conjugate vaccine could overwhelm available storage and transport refrigerator space, creating bottlenecks that would prevent the flow of vaccines down to the clinics. As a result, the availability of all World Health Organization Expanded Program on Immunization vaccines to patients might decrease from an average of 69% to 28.2% (range = 10%-51%). Addition of refrigerator and transport capacity could alleviate this bottleneck. CONCLUSIONS Our results suggest that the effects on the vaccine supply chain should be considered when introducing a new vaccine and that computational models can help assess evolving needs and prevent problems with vaccine delivery.

Risk of dengue for tourists and teams during the World Cup 2014 in Brazil.

Significance Persons living in the tropics and subtropics are at risk for dengue fever and dengue hemorrhagic fever, and large epidemics occur unexpectedly that can overburden healthcare systems. The spatial and temporal dynamics of dengue transmission are poorly understood, limiting disease control efforts. We compiled a large-scale dataset and analyzed continental-scale patterns of dengue in Southeast Asia. Our analysis shows that periods of elevated temperatures can drive the occurrence of synchronous dengue epidemics across the region. This multicountry collaborative study improved insight that may lead to improved prediction of dengue transmission patterns and more effective disease surveillance and control efforts. Dengue is a mosquito-transmitted virus infection that causes epidemics of febrile illness and hemorrhagic fever across the tropics and subtropics worldwide. Annual epidemics are commonly observed, but there is substantial spatiotemporal heterogeneity in intensity. A better understanding of this heterogeneity in dengue transmission could lead to improved epidemic prediction and disease control. Time series decomposition methods enable the isolation and study of temporal epidemic dynamics with a specific periodicity (e.g., annual cycles related to climatic drivers and multiannual cycles caused by dynamics in population immunity). We collected and analyzed up to 18 y of monthly dengue surveillance reports on a total of 3.5 million reported dengue cases from 273 provinces in eight countries in Southeast Asia, covering ∼107 km2. We detected strong patterns of synchronous dengue transmission across the entire region, most markedly during a period of high incidence in 1997–1998, which was followed by a period of extremely low incidence in 2001–2002. This synchrony in dengue incidence coincided with elevated temperatures throughout the region in 1997–1998 and the strongest El Niño episode of the century. Multiannual dengue cycles (2–5 y) were highly coherent with the Oceanic Niño Index, and synchrony of these cycles increased with temperature. We also detected localized traveling waves of multiannual dengue epidemic cycles in Thailand, Laos, and the Philippines that were dependent on temperature. This study reveals forcing mechanisms that drive synchronization of dengue epidemics on a continental scale across Southeast Asia.