  Network diffusion models are used to study things like disease transmission,
information spread, and technology adoption. However, small amounts of
mismeasurement are extremely likely in the networks constructed to
operationalize these models. We show that estimates of diffusions are highly
non-robust to this measurement error. First, we show that even when measurement
error is vanishingly small, such that the share of missed links is close to
zero, forecasts about the extent of diffusion will greatly underestimate the
truth. Second, a small mismeasurement in the identity of the initial seed
generates a large shift in the locations of expected diffusion path. We show
that both of these results still hold when the vanishing measurement error is
only local in nature. Such non-robustness in forecasting exists even under
conditions where the basic reproductive number is consistently estimable.
Possible solutions, such as estimating the measurement error or implementing
widespread detection efforts, still face difficulties because the number of
missed links are so small. Finally, we conduct Monte Carlo simulations on
simulated networks, and real networks from three settings: travel data from the
COVID-19 pandemic in the western US, a mobile phone marketing campaign in rural
India, and in an insurance experiment in China.
